AWS DBS真题 No.1-100

正确答案: B
Correct answer is B as a manual snapshot needs to be taken to be able to restore Redshift to the point before upgrade.,Refer AWS documentation – Redshift Snapshots,Snapshots are point-in-time backups of a cluster. There are two types of snapshots: automated and manual. Amazon Redshift stores these snapshots internally in Amazon S3 by using an encrypted Secure Sockets Layer (SSL) connection.,Amazon Redshift automatically takes incremental snapshots that track changes to the cluster since the previous automated snapshot. Automated snapshots retain all of the data required to restore a cluster from a snapshot. You can create a snapshot schedule to control when automated snapshots are taken, or you can take a manual snapshot any time.,When you restore from a snapshot, Amazon Redshift creates a new cluster and makes the new cluster available before all of the data is loaded, so you can begin querying the new cluster immediately. The cluster streams data on demand from the snapshot in response to active queries, then loads the remaining data in the background.,When you launch a cluster, you can set the retention period for automated and manual snapshots. You can change the retention period for automated and manual snapshots by modifying the cluster. You can change the retention period for a manual snapshot when you create the snapshot or by modifying the snapshot.,You can take a manual snapshot any time. By default, manual snapshots are retained indefinitely, even after you delete your cluster. You can specify the retention period when you create a manual snapshot, or you can change the retention period by modifying the snapshot. If you create a snapshot using the Amazon Redshift console, it defaults the snapshot retention period to 365 days.,If a snapshot is deleted, you can’t start any new operations that reference that snapshot. However, if a restore operation is in progress, that restore operation will run to completion.,Option A is wrong as it would only copy the data.,Option C is wrong as you cannot copy the automated snapshot. Also, automated snapshot are controlled by time and size and would not represent the data before upgrade.,Option D is wrong as waitForSnapshotAvailable needs to be called after triggering the manual snapshot creation and it can be verified from console as well.

正确答案: B

正确答案是B，因为需要手动创建快照才能恢复Redshift到升级前的状态。

参考AWS文档 – Redshift快照。

快照是集群的时间点备份。快照有两种类型：自动快照和手动快照。Amazon Redshift通过使用加密的安全套接字层（SSL）连接，将这些快照存储在Amazon S3内部。

Amazon Redshift会自动创建增量快照，跟踪自上一个自动快照以来对集群所做的更改。自动快照保留所有恢复集群所需的数据。您可以创建快照计划，以控制自动快照的创建时间，或者可以随时创建手动快照。

当您从快照恢复时，Amazon Redshift会创建一个新集群，并在所有数据加载完成之前使新集群可用，这样您就可以立即开始查询新集群。集群按需从快照中流式传输数据，以响应活跃查询，然后在后台加载其余数据。

当您启动集群时，您可以设置自动快照和手动快照的保留期。您可以通过修改集群来更改自动快照和手动快照的保留期。您可以在创建手动快照时设置保留期，或者通过修改快照来更改保留期。

您可以随时创建手动快照。默认情况下，手动快照会被永久保留，即使您删除集群也不会被删除。您可以在创建手动快照时指定保留期，或者通过修改快照来更改保留期。如果您使用Amazon Redshift控制台创建快照，它默认将快照的保留期设置为365天。

如果快照被删除，您无法启动任何引用该快照的新操作。然而，如果恢复操作正在进行，该恢复操作将运行至完成。

选项A是错误的，因为它只是复制数据。

选项C是错误的，因为您不能复制自动快照。此外，自动快照受时间和大小的控制，不会代表升级前的数据。

选项D是错误的，因为waitForSnapshotAvailable必须在触发手动快照创建后调用，并且也可以从控制台验证。

正确答案: A
Correct answer is A as we need to tackle 2 issues. First is to capture real time sensor data and store it for analysis. Second is to respond to emergency notifications events with low latency. First can be handled using Kinesis Firehose to load data in Redshift for analysis. Second can be handled using SQS for notifications and DynamoDB for quick analysis or processing.,Refer AWS documentation – Kinesis Firehose FAQs,Amazon Kinesis Data Firehose buffers incoming streaming data to a certain size or for a certain period of time before delivering it to destinations. You can configure buffer size and buffer interval while creating your delivery stream. Buffer size is in MBs and ranges from 1MB to 128MB for Amazon S3 destination and 1MB to 100MB for Amazon Elasticsearch Service destination. Buffer interval is in seconds and ranges from 60 seconds to 900 seconds. Please note that in circumstances where data delivery to destination is falling behind data writing to delivery stream, Firehose raises buffer size dynamically to catch up and make sure that all data is delivered to the destination.,Option B is wrong as SQS is not suitable for real time sensor data collection and not is DynamoDB for analytics. Also, Redshift with Kinesis would not provide quick handling for data as Kinesis works on buffer interval and buffer size.,Option C is wrong as DynamoDB is not ideal for batch analytics.,Option D is wrong as Kinesis would not work emergency services handling as it works on buffer interval and buffer size.

正确答案: A

正确答案是A，因为我们需要解决两个问题。第一个是捕获实时传感器数据并将其存储以供分析。第二个是以低延迟响应紧急通知事件。第一个问题可以使用Kinesis Firehose将数据加载到Redshift中进行分析。第二个问题可以使用SQS处理通知，使用DynamoDB进行快速分析或处理。

参考AWS文档 – Kinesis Firehose常见问题解答

Amazon Kinesis Data Firehose在将数据传输到目标之前，会将传入的流数据缓冲到一定的大小或一定的时间段。您可以在创建传输流时配置缓冲区大小和缓冲区间隔。缓冲区大小以MB为单位，对于Amazon S3目标范围为1MB到128MB，对于Amazon Elasticsearch Service目标范围为1MB到100MB。缓冲区间隔以秒为单位，范围为60秒到900秒。请注意，在数据传输到目标的速度落后于写入传输流的数据时，Firehose会动态增加缓冲区大小，以赶上并确保所有数据都传送到目标。

选项B是错误的，因为SQS不适合实时传感器数据收集，DynamoDB也不适合分析。此外，Redshift与Kinesis的组合无法提供快速的数据处理，因为Kinesis依赖于缓冲区间隔和缓冲区大小。

选项C是错误的，因为DynamoDB不适合批量分析。

选项D是错误的，因为Kinesis不适用于紧急服务处理，因为它依赖于缓冲区间隔和缓冲区大小。

正确答案: A, C, E
Correct answers are A, C & E,Option A as KMS keys are specific to the region and a new key needs to be created in the destination region.,Option C as the grant needs to be provided for Redshift to use the master key in the destination region,Option E as the replication needs to be enabled on the source region.,Refer AWS documentation – Cross Region KMS Encrypted Snapshot & Redshift – Copying AWS KMS-Encrypted Snapshots to Another AWS Region,When you launch an Amazon Redshift cluster, you can choose to encrypt it with a master key from the AWS Key Management Service (AWS KMS). AWS KMS keys are specific to a region. If you want to enable cross-region snapshot copy for an AWS KMS-encrypted cluster, you must configure a snapshot copy grant for a master key in the destination region so that Amazon Redshift can perform encryption operations in the destination region.,AWS KMS keys are specific to an AWS Region. If you enable copying of Amazon Redshift snapshots to another AWS Region, and the source cluster and its snapshots are encrypted using a master key from AWS KMS, you need to configure a grant for Amazon Redshift to use a master key in the destination AWS Region. This grant enables Amazon Redshift to encrypt snapshots in the destination AWS Region.,Option B is wrong as keys are specific to the region, new keys need to be created.,Option D is wrong as the grants need to be provided in the destination region,Option F is wrong as the cross-region replication needs to be enabled in the source region.

正确答案: A, C, E

正确答案是A, C和E，选项A是因为KMS密钥是区域特定的，需要在目标区域创建新的密钥。选项C是因为需要为Redshift提供授权，以便在目标区域使用主密钥。选项E是因为需要在源区域启用复制。

参考AWS文档 – 跨区域KMS加密快照和Redshift – 将AWS KMS加密快照复制到另一个AWS区域。

当您启动Amazon Redshift集群时，您可以选择使用来自AWS密钥管理服务(AWS KMS)的主密钥对其进行加密。AWS KMS密钥是特定于某个区域的。如果您希望启用AWS KMS加密集群的跨区域快照复制，必须为目标区域的主密钥配置快照复制授权，以便Amazon Redshift可以在目标区域执行加密操作。

AWS KMS密钥是特定于AWS区域的。如果您启用将Amazon Redshift快照复制到另一个AWS区域，并且源集群及其快照使用AWS KMS的主密钥加密，则需要为Amazon Redshift配置授权，以便在目标AWS区域使用主密钥。此授权使Amazon Redshift能够在目标AWS区域加密快照。

选项B是错误的，因为密钥是特定于区域的，需要创建新的密钥。

选项D是错误的，因为授权需要在目标区域提供。

选项F是错误的，因为需要在源区域启用跨区域复制。

正确答案: B
Correct answer is B as Redshift workload management allows proper usage of cluster.,Refer to the AWS Blog for Redshift to run mixed workloads,Amazon Redshift Workload Management allows you to manage workloads of various sizes and complexity for specific environments. Parameter groups contain WLM configuration, which determines how many query queues are available for processing and how queries are routed to those queues. Following settings are available

正确答案: B

正确答案是B，因为Redshift工作负载管理允许集群的正确使用。请参阅AWS博客了解Redshift如何运行混合工作负载。

Amazon Redshift工作负载管理允许您管理不同规模和复杂度的工作负载，适用于特定环境。参数组包含WLM配置，决定了可用于处理的查询队列数量，以及查询如何被路由到这些队列。以下设置可用：

正确答案: A
Correct answer is A as the simplest way to build the model is to use the Amazon Machine Learning (Amazon ML), using the binary classification model. As the company has historical data they can learn from and apply it from the new data.,Refer AWS documentation – Predicting Customer Churn with Amazon Machine Learning,Options B, C & D are wrong as they do not provide models or methods to apply to new data.

正确答案: A

正确答案是A，因为构建模型的最简单方法是使用Amazon机器学习(Amazon ML)，并采用二分类模型。由于公司有历史数据可以学习并将其应用于新数据。

参考AWS文档 – 使用Amazon机器学习预测客户流失。

选项B、C和D是错误的，因为它们没有提供可以应用于新数据的模型或方法。

正确答案: B
Correct answer is B as the point here is durable data store with any time analytics the best option is to store the data in DynamoDB and use Apache Hive with Amazon Elastic MapReduce for analytics.,Refer AWS documentation – DynamoDB EMR Hive Processing,Option A is wrong as Elastic Block Store is not ideal for storing social media data,Option C is wrong as Amazon Glacier is not an ideal for storing social media data,Option D is wrong as Elastic Block Store is not ideal for storing social media data and CloudWatch is not for analytics.

正确答案: B

正确答案是B，因为这里的关键是持久化数据存储和随时分析，最佳选择是将数据存储在DynamoDB中，并使用Apache Hive和Amazon Elastic MapReduce进行分析。参考AWS文档 – DynamoDB EMR Hive处理。

选项A是错误的，因为Elastic Block Store不适合存储社交媒体数据。

选项C是错误的，因为Amazon Glacier不适合存储社交媒体数据。

选项D是错误的，因为Elastic Block Store不适合存储社交媒体数据，且CloudWatch不用于分析。

正确答案: A
Correct answer is A as S3 can provide the most cost-effective solution to store data while providing highly durable and highly flexible storage option with respect to the type and structure of data.,Option B is wrong and HDFS would not be a cost-effective option as compared to S3.,Options C & D are wrong as they do not have flexibility in terms of data type and structure and would not be cost-effective as well.

正确答案: A

正确答案是A，因为S3可以提供最具成本效益的数据存储解决方案，同时在数据类型和结构方面提供高度耐用和灵活的存储选项。

选项B是错误的，HDFS与S3相比不会是一个具有成本效益的选择。

选项C和D是错误的，因为它们在数据类型和结构方面缺乏灵活性，也不会是具有成本效益的选择。

正确答案: D
Correct answer is D as Data Pipeline can be used to import the data from MySQL and Export it to DynamoDB as batch.,Refer AWS documentation – Near Zero Downtime Migration from MySQL to DynamoDB, Data Pipeline Export MySQL & Data Pipeline Import DynamoDB,Option A is wrong as Kinesis data stream cannot emit data directly to DynamoDB table and would need a consumer. Also Kinesis is best for real-time puts,Option B is wrong as it doesn’t define how the migration is happening,Option C is wrong as You do not have access to RDS instance.

正确答案: D

正确答案是 D，因为数据管道可以用来从 MySQL 导入数据，并将其作为批量导出到 DynamoDB。请参考 AWS 文档 – 从 MySQL 到 DynamoDB 的近零停机迁移，数据管道导出 MySQL 和数据管道导入 DynamoDB。

选项 A 错误，因为 Kinesis 数据流不能直接将数据发送到 DynamoDB 表，并且需要一个消费者。此外，Kinesis 最适合实时写入。

选项 B 错误，因为它没有定义迁移的具体方式。

选项 C 错误，因为您无法访问 RDS 实例。

正确答案: A, D
Correct answer are A & D as Redshift Enhanced VPC Routing helps access AWS services including S3 through VPC, without having to route any traffic through internet. Also, note the region is the same.,Refer AWS documentation – Redshift Enhanced VPC Routing,When you use Amazon Redshift Enhanced VPC Routing, Amazon Redshift forces all COPY and UNLOAD traffic between your cluster and your data repositories through your Amazon VPC. You can now use standard VPC features, such as VPC security groups, network access control lists (ACLs), VPC endpoints, VPC endpoint policies, Internet gateways, and Domain Name System (DNS) servers, to tightly manage the flow of data between your Amazon Redshift cluster and other resources. When you use Enhanced VPC Routing to route traffic through your VPC, you can also use VPC flow logs to monitor COPY and UNLOAD traffic.,If Enhanced VPC Routing is not enabled, Amazon Redshift routes traffic through the Internet, including traffic to other services within the AWS network.,VPC Endpoints – For traffic to an Amazon S3 bucket in the same region as your cluster, you can create a VPC endpoint to direct traffic directly to the bucket. When you use VPC endpoints, you can attach an endpoint policy to manage access to Amazon S3.,Option B is wrong as Redshift cannot directly access S3 without internet,Option C is wrong as NAT enables connectivity to services via Internet only or other AWS services.,NAT gateway – To connect to an Amazon S3 bucket in another region or to another service within the AWS network, or to access a host instance outside the AWS network, you can configure a network address translation (NAT) gateway.

正确答案: A, D

正确答案是 A 和 D，因为 Redshift 增强型 VPC 路由帮助通过 VPC 访问 AWS 服务，包括 S3，而无需通过互联网路由任何流量。另请注意，区域是相同的。

参考 AWS 文档 – Redshift 增强型 VPC 路由。

当您使用 Amazon Redshift 增强型 VPC 路由时，Amazon Redshift 强制所有 COPY 和 UNLOAD 流量通过您的 Amazon VPC 在您的集群与数据存储库之间传输。现在，您可以使用标准 VPC 功能，如 VPC 安全组、网络访问控制列表（ACLs）、VPC 终端节点、VPC 终端节点策略、互联网网关和域名系统（DNS）服务器，来严格管理您的 Amazon Redshift 集群与其他资源之间的数据流动。当您使用增强型 VPC 路由通过您的 VPC 路由流量时，您还可以使用 VPC 流日志来监控 COPY 和 UNLOAD 流量。

如果没有启用增强型 VPC 路由，Amazon Redshift 会通过互联网路由流量，包括到 AWS 网络内其他服务的流量。

VPC 终端节点 – 对于与集群位于同一区域的 Amazon S3 存储桶的流量，您可以创建 VPC 终端节点将流量直接引导到存储桶。当您使用 VPC 终端节点时，可以附加终端节点策略来管理对 Amazon S3 的访问。

选项 B 错误，因为 Redshift 无法在没有互联网的情况下直接访问 S3。

选项 C 错误，因为 NAT 仅通过互联网或其他 AWS 服务启用对服务的连接。

NAT 网关 – 若要连接到另一区域的 Amazon S3 存储桶或连接到 AWS 网络内的其他服务，或者访问 AWS 网络外的主机实例，您可以配置网络地址转换（NAT）网关。

正确答案: B
Correct answer is B as DynamoDB Auto Scaling can help scale as per the demand.,Refer AWS documentation – DynamoDB AutoScaling,Many database workloads are cyclical in nature or are difficult to predict in advance. For example, consider a social networking app where most of the users are active during daytime hours. The database must be able to handle the daytime activity, but there’s no need for the same levels of throughput at night. Another example might be a new mobile gaming app that is experiencing rapid adoption. If the game becomes too popular, it could exceed the available database resources, resulting in slow performance and unhappy customers. These kinds of workloads often require manual intervention to scale database resources up or down in response to varying usage levels.,DynamoDB auto scaling uses the AWS Application Auto Scaling service to dynamically adjust provisioned throughput capacity on your behalf, in response to actual traffic patterns. This enables a table or a global secondary index to increase its provisioned read and write capacity to handle sudden increases in traffic, without throttling. When the workload decreases, Application Auto Scaling decreases the throughput so that you don’t pay for unused provisioned capacity.,Option A is wrong as its more of a manual effort and not a cost-effective way as compared to B,Option C is wrong as DynamoDB streams captures a time-ordered sequence of item-level modifications in any DynamoDB table, and stores this information in a log for up to 24 hours. It cannot help review the current capacity.,Option D is wrong as it is not a cost effective to provision the throughput to the maximum required.

正确答案: B

正确答案是B，因为DynamoDB自动扩展可以根据需求进行扩展。参见AWS文档 – DynamoDB自动扩展。

许多数据库工作负载具有周期性特征，或者很难提前预测。例如，考虑一个社交网络应用，大多数用户在白天活跃。数据库必须能够处理白天的活动，但晚上无需相同水平的吞吐量。另一个例子可能是一个新兴的手机游戏应用，正在经历快速增长。如果游戏变得过于受欢迎，它可能会超出可用的数据库资源，导致性能缓慢和客户不满。这些类型的工作负载通常需要人工干预来根据使用水平的变化扩展或缩减数据库资源。

DynamoDB自动扩展使用AWS应用程序自动扩展服务，根据实际的流量模式动态调整您预配置的吞吐容量。这使得表或全局二级索引能够增加其预配置的读写容量，以处理突如其来的流量增加，而不会出现限流。当工作负载减少时，应用程序自动扩展会减少吞吐量，以避免为未使用的预配置容量付费。

选项A是错误的，因为这更多是人工操作，相比B来说并不是一种具有成本效益的方法。

选项C是错误的，因为DynamoDB流捕获了任何DynamoDB表中按时间顺序排列的项级修改，并将这些信息存储在日志中，最多24小时。它不能帮助审查当前容量。

选项D是错误的，因为将吞吐量预配置到最大需求并不是一种具有成本效益的方法。

正确答案: A
Correct answer is A as Presto allows ad hoc query analysis over multiple data sources.,Refer AWS documentation – Presto,Presto (or PrestoDB) is an open source, distributed SQL query engine, designed from the ground up for fast analytic queries against data of any size. It supports both non-relational sources, such as the Hadoop Distributed File System (HDFS), Amazon S3, Cassandra, MongoDB, and HBase, and relational data sources such as MySQL, PostgreSQL, Amazon Redshift, Microsoft SQL Server, and Teradata.,Presto can query data where it is stored, without needing to move data into a separate analytics system. Query execution runs in parallel over a pure memory-based architecture, with most results returning in seconds.,Option B is wrong as QuickSight is a fast, cloud-powered business intelligence service that makes it easy to deliver insights to everyone in your organization.,Option C is wrong as Ganglia is a scalable distributed monitoring system for high-performance computing systems such as clusters and Grids,Option D is wrong as YARN is the resource management and job scheduling technology in the open source Hadoop distributed processing framework.

正确答案: A

正确答案是A，因为Presto允许在多个数据源上进行临时查询分析。参考AWS文档 – Presto。

Presto（或PrestoDB）是一个开源的分布式SQL查询引擎，专为快速分析查询设计，支持处理任何大小的数据。它支持非关系型数据源，如Hadoop分布式文件系统（HDFS）、Amazon S3、Cassandra、MongoDB和HBase，以及关系型数据源，如MySQL、PostgreSQL、Amazon Redshift、Microsoft SQL Server和Teradata。

Presto可以在数据存储的位置进行查询，无需将数据移入单独的分析系统。查询执行在纯内存架构上并行运行，大部分结果在几秒钟内返回。

选项B是错误的，因为QuickSight是一个快速的、云支持的商业智能服务，使得将洞察力轻松传递给组织中的每个人变得容易。

选项C是错误的，因为Ganglia是一个可扩展的分布式监控系统，适用于高性能计算系统，如集群和Grid。

选项D是错误的，因为YARN是开源Hadoop分布式处理框架中的资源管理和作业调度技术。

正确答案: C
Correct answer is C as Amazon DynamoDB is a fully managed NoSQL database service that provides fast and predictable performance with seamless scalability.,Option A is wrong as Amazon Aurora (Aurora) is a fully managed relational database engine that’s compatible with MySQL and PostgreSQL. Amazon Aurora supports relational data models and does not support graph model.,Option B is wrong as Amazon Relational Database Service (Amazon RDS) is a web service that makes it easier to set up, operate, and scale a relational database in the cloud. Amazon RDS supports relational data models and does not support graph model.,Option D is wrong as Amazon Neptune is a fast, reliable, fully managed graph database service that makes it easy to build and run applications that work with highly connected datasets.

正确答案: C

正确答案是C，因为Amazon DynamoDB是一个完全托管的NoSQL数据库服务，提供快速和可预测的性能，并具有无缝的可扩展性。

选项A是错误的，因为Amazon Aurora（Aurora）是一个完全托管的关系数据库引擎，兼容MySQL和PostgreSQL。Amazon Aurora支持关系数据模型，但不支持图形模型。

选项B是错误的，因为Amazon关系数据库服务（Amazon RDS）是一个Web服务，使得在云中设置、操作和扩展关系数据库变得更加容易。Amazon RDS支持关系数据模型，但不支持图形模型。

选项D是错误的，因为Amazon Neptune是一个快速、可靠、完全托管的图形数据库服务，使得构建和运行与高度连接的数据集交互的应用变得容易。

正确答案: A
Correct answer is A as the key requirement here is DR with RTO of 2 hours and a RPO of 24 hours with only the changed items to be replicated. DynamoDB cross region copy would help for DR with required RPO and RTO with Lastupdated time would help replicate only updated items.,Refer AWS DynamoDB Data Copy Between Regions Blog,Option B is wrong the scan operation is expensive and time consuming and would not help meet RTO. Also, there is no handling for only updated data.,Option C is wrong is time consuming and would not help meet the RTO. Also, there is no handling for only updated data.,Option D is wrong as this needs update to the application to push data to DynamoDB as well the SQS in a reliable manner.

正确答案: A

正确答案是A，因为这里的关键要求是DR，RTO为2小时，RPO为24小时，并且只复制已更改的项目。DynamoDB跨区域复制可以帮助满足DR要求，同时提供所需的RPO和RTO，Lastupdated时间将帮助仅复制更新的项目。参考AWS DynamoDB跨区域数据复制博客。

选项B是错误的，扫描操作开销大且耗时，不会帮助满足RTO。同时，无法处理仅更新的数据。

选项C是错误的，耗时且无法帮助满足RTO。同时，无法处理仅更新的数据。

选项D是错误的，因为这需要更新应用程序，将数据以可靠的方式推送到DynamoDB以及SQS。

正确答案: A
Correct answer is A as Kinesis Data Firehose can be used to transfer data directly to Elasticsearch, without any handling.,Refer AWS documentation – Kinesis Firehose,Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can also batch, compress, transform, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security.,Option B is wrong as EMR is for batch analytics.,Option C is wrong as AWS Data Pipeline does not capture real time data. AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals.,Option D is wrong as SQS is a message service and would need handling to storage to Elasticsearch.

正确答案: A

正确答案是 A，因为 Kinesis Data Firehose 可以直接将数据传输到 Elasticsearch，而无需任何处理。参考 AWS 文档 – Kinesis Firehose。

Amazon Kinesis Data Firehose 是最简单、可靠地将流数据加载到数据存储和分析工具中的方式。它可以捕获、转换并将流数据加载到 Amazon S3、Amazon Redshift、Amazon Elasticsearch Service 和 Splunk，使得您现有的业务智能工具和仪表盘能够进行近实时分析。它是一个完全托管的服务，能够自动扩展以匹配您的数据吞吐量，并且无需持续管理。它还可以在加载之前对数据进行批处理、压缩、转换和加密，从而最小化目标存储使用的空间并提高安全性。

选项 B 错误，因为 EMR 是用于批量分析的。

选项 C 错误，因为 AWS Data Pipeline 不捕获实时数据。AWS Data Pipeline 是一个网络服务，帮助您在指定的时间间隔内可靠地处理和移动不同 AWS 计算和存储服务之间的数据，以及本地数据源。

选项 D 错误，因为 SQS 是一个消息服务，需要处理才能将数据存储到 Elasticsearch。

正确答案: B
Correct answer is B as S3 Server Side Encryption with S3 Managed Keys provide encryption. S3 ACLs allows fine grained control access and S3 to access logs would help provide traceability across all tools.,Use Server-Side Encryption with Amazon S3-Managed Keys (SSE-S3) – Each object is encrypted with a unique key. As an additional safeguard, it encrypts the key itself with a master key that it regularly rotates. Amazon S3 server-side encryption uses one of the strongest block ciphers available, 256-bit Advanced Encryption Standard (AES-256), to encrypt your data.,Option C is wrong as with Client-Side Encryption, the users must have the keys to decrypt the data.,When downloading an object—The client downloads the encrypted object from Amazon S3. Using the material description from the object’s metadata, the client determines which master key to use to decrypt the data key. The client uses that master key to decrypt the data key and then uses the data key to decrypt the object.,Options A & D are wrong as KMS Grants are mainly to provide access to the KMS keys. There is not mention of fine grained control over the S3 objects.

正确答案: B

正确答案是B，因为S3服务器端加密使用S3管理的密钥提供加密功能。S3访问控制列表（ACLs）允许精细控制访问，S3访问日志有助于提供跨所有工具的可追溯性。

使用Amazon S3管理密钥的服务器端加密（SSE-S3） – 每个对象都会使用唯一的密钥进行加密。作为额外的保障，它会使用定期轮换的主密钥加密该密钥本身。Amazon S3服务器端加密使用最强的块加密算法之一，256位高级加密标准（AES-256）来加密您的数据。

选项C是错误的，因为在客户端加密中，用户必须拥有密钥才能解密数据。

当下载一个对象时——客户端从Amazon S3下载加密对象。通过使用对象元数据中的材料描述，客户端确定使用哪个主密钥来解密数据密钥。客户端使用该主密钥解密数据密钥，然后使用数据密钥解密对象。

选项A和D是错误的，因为KMS授权主要是提供对KMS密钥的访问权限。没有提到对S3对象的精细控制。

正确答案: B
Correct answer is B as it is essential when writing mobile applications that you consider the security of both how the application authenticates and how it stores credentials. Amazon Cognito gives you the ability to securely authenticate pools of users on any type of device at scale.,Option A is wrong as it uses an anonymous Put, which may allow other apps to write counterfeit data;,Option C is wrong as it would put credentials directly into the application, which is strongly discouraged because applications can be decompiled which can compromise the keys.,Option D is wrong as it does not meet our availability requirements: although the EC2 instances are running in different Availability Zones, the EBS volumes attached to each instance only store data in a single Availability Zone.

正确答案: B

正确答案是B，因为在编写移动应用程序时，考虑到应用程序的认证方式和凭证存储方式的安全性至关重要。Amazon Cognito使您能够在任何类型的设备上大规模安全地验证用户池。

选项A是错误的，因为它使用了匿名Put，这可能允许其他应用程序写入伪造数据。

选项C是错误的，因为它会将凭证直接放入应用程序中，这是强烈不建议的，因为应用程序可以被反编译，从而泄露密钥。

选项D是错误的，因为它没有满足我们的可用性要求：尽管EC2实例运行在不同的可用区，但附加到每个实例的EBS卷仅在单一可用区内存储数据。

正确答案: D
Correct answer is D as you need to represent time driven data for demand trends over multiple months, Line chart would be an ideal choice.,Refer AWS documentation – QuickSight Visual Types,Option A is wrong as Scatter plots can help to visualize two or three measures for a dimension.,Option B is wrong as Pie charts can help to compare values for items in a dimension.,Option C is wrong as Pivot tables can help to show measure values for the intersection of two dimensions.

正确答案: D

正确答案是 D，因为你需要表示跨多个月份的需求趋势的时间驱动数据，折线图是理想的选择。

参考 AWS 文档 – QuickSight 可视化类型。

选项 A 错误，因为散点图可以帮助可视化一个维度的两个或三个度量。

选项 B 错误，因为饼图可以帮助比较维度中项目的值。

选项 C 错误，因为透视表可以帮助显示两个维度交集的度量值。

正确答案: A, B, E
Correct answers are A, B & E,Refer to the AWS S3 Protecting Data using Encryption,Data at rest encryption using S3 can be implemented using either Server Side or Client Side encryption. SSE can be implemented using either KMS provided keys (SSE-KMS) or Customer provided keys (SSE-C). CSE can be implemented by encrypting the data before uploading it to S3 and then decrypting the data after downloading it from S3 at client side.,Option C is wrong as server side encryption doesn’t work with EC2 key pair,Option D is wrong as bucket policies are just to restrict access to S3,Option F is wrong as it targets the data in transit only.

正确答案: A, B, E

正确答案是 A, B 和 E，参见 AWS S3 使用加密保护数据，使用 S3 的静态数据加密可以通过服务器端加密（SSE）或客户端加密（CSE）来实现。SSE 可以通过使用 KMS 提供的密钥（SSE-KMS）或客户提供的密钥（SSE-C）来实现。CSE 可以通过在上传数据到 S3 之前加密数据，然后在从 S3 下载数据后在客户端解密数据来实现。

选项 C 错误，因为服务器端加密不适用于 EC2 密钥对。

选项 D 错误，因为存储桶策略只是用来限制对 S3 的访问。

选项 F 错误，因为它仅针对传输中的数据。

正确答案: A
Correct answer is A as
D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.,Option B is wrong as Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. It is not a rich visualization tool which supports all the web standards.,Option C is wrong as RStudio makes R easier to use. It includes a code editor, debugging & visualization tools.,Option D is wrong as Hue is the open source analytics workbench designed for fast data discovery, intelligent query assistance, and seamless collaboration

正确答案: A

Correct answer is A as D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation.

Option B is wrong as Jupyter is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more. It is not a rich visualization tool which supports all the web standards.

Option C is wrong as RStudio makes R easier to use. It includes a code editor, debugging & visualization tools.

Option D is wrong as Hue is the open source analytics workbench designed for fast data discovery, intelligent query assistance, and seamless collaboration.

正确答案: A
Correct answer is A as the customer is planning to use Dense Storage nodes, they can start with more number of small nodes which would be cost-effective as compared to large nodes and easier to improve query performance and storage.,Refer AWS documentation – Redshift Cluster & Nodes,DS2 node types are optimized for large data workloads and use hard disk drive (HDD) storage. Node types are available in different sizes. DS2 nodes are available in xlarge and 8xlarge sizes.,The number of nodes that you choose depends on the size of your dataset and your desired query performance. Using the dense storage node types as an example, if you have 32 TB of data, you can choose either 16 ds2.xlarge nodes or 2 ds2.8xlarge nodes. If your data grows in small increments, choosing the ds2.xlarge node size allows you to scale in increments of 2 TB. If you typically see data growth in larger increments, a ds2.8xlarge node size might be a better choice.,Because Amazon Redshift distributes and executes queries in parallel across all of a cluster’s compute nodes, you can increase query performance by adding nodes to your cluster. Amazon Redshift also distributes your data across all compute nodes in a cluster. When you run a cluster with at least two compute nodes, data on each node will always be mirrored on disks on another node and you reduce the risk of incurring data loss.,Option B is wrong as with 50TB you would need 4 large nodes and it would not be as cost effective as small nodes.,Options C & D are wrong as it is not meet the customer requirement of limited budget and avoid performing tests unless absolutely needed.

正确答案：A

正确答案是 A，因为客户计划使用密集存储节点，他们可以从更多的小型节点开始，这相比于大型节点更具成本效益，并且更容易提高查询性能和存储能力。

请参考 AWS 文档 – Redshift 集群与节点。DS2 节点类型针对大型数据工作负载进行了优化，并使用硬盘驱动器（HDD）存储。节点类型提供不同的尺寸，DS2 节点可用的尺寸包括 xlarge 和 8xlarge。

您选择的节点数量取决于数据集的大小以及所期望的查询性能。例如，使用密集存储节点类型，如果您的数据量为 32 TB，您可以选择 16 个 ds2.xlarge 节点或 2 个 ds2.8xlarge 节点。如果您的数据增长是小幅递增的，那么选择 ds2.xlarge 节点大小可以让您以 2 TB 的增量扩展。如果您的数据通常以较大增量增长，那么 ds2.8xlarge 节点大小可能是更好的选择。

由于 Amazon Redshift 在集群的所有计算节点上并行分布和执行查询，因此您可以通过向集群添加节点来提高查询性能。Amazon Redshift 还会将您的数据分布到集群中的所有计算节点。当您的集群至少有两个计算节点时，每个节点上的数据都会始终镜像到另一个节点的磁盘上，从而降低数据丢失的风险。

选项 B 是错误的，因为对于 50TB 数据，您需要 4 个大型节点，而这相比于小型节点并不具备成本效益。

选项 C 和 D 是错误的，因为它们不符合客户有限预算的要求，并且需要尽量避免进行测试，除非绝对必要。

正确答案: D
Correct answer is D as the requirement is real time data ingestion and analytics, the best option is to use Kinesis for storing the real time incoming data. The data can then be moved to S3 and analyzed using EMR and Redshift. Data can then be moved to Glacier for archival.,Refer AWS documentation – Kinesis,Amazon Kinesis is a platform for streaming data on AWS, making it easy to load and analyze streaming data, and also providing the ability for you to build custom streaming data applications for specialized needs.,Option A is wrong as Cognito is not suitable for handling real time data,Amazon Cognito lets you easily add user sign-up and sign-in and manage permissions for your mobile and web apps. You can create your own user directory within Amazon Cognito, or you can authenticate users through social identity providers such as Facebook, Twitter, or Amazon; with SAML identity solutions; or by using your own identity system. In addition, Amazon Cognito enables you to save data locally on users’ devices, allowing your applications to work even when the devices are offline. You can then synchronize data across users’ devices so that their app experience remains consistent regardless of the device they use.,Option B is wrong as DynamoDB is not suitable for streaming data ingestion and handling.,Option C is wrong as S3 is not an ideal solution to handle this huge amount of requests.

正确答案: D

正确答案是 D，因为要求是实时数据摄取和分析，最佳选择是使用 Kinesis 存储实时传入的数据。然后可以将数据移动到 S3，并使用 EMR 和 Redshift 进行分析。数据随后可以移动到 Glacier 进行归档。

参考 AWS 文档 – Kinesis

Amazon Kinesis 是一个用于在 AWS 上进行流数据处理的平台，它使加载和分析流数据变得更加简单，并且提供了为特定需求构建自定义流数据应用程序的能力。

选项 A 错误，因为 Cognito 不适合处理实时数据。

Amazon Cognito 让你轻松地为你的移动应用和 Web 应用添加用户注册和登录功能，并管理权限。你可以在 Amazon Cognito 中创建自己的用户目录，或者通过社交身份提供商（如 Facebook、Twitter 或 Amazon）验证用户；使用 SAML 身份解决方案；或使用你自己的身份系统。此外，Amazon Cognito 使你能够将数据本地保存到用户的设备上，允许你的应用即使在设备离线时也能工作。然后你可以在用户的设备之间同步数据，使他们的应用体验无论使用哪个设备都保持一致。

选项 B 错误，因为 DynamoDB 不适合流数据的摄取和处理。

选项 C 错误，因为 S3 不是处理大量请求的理想解决方案。

正确答案: D
Correct answer is D as Zeppelin provides data ingestion, data discovery, data analytics and data visualization & collaboration.,Refer documentation – Zeppelin,Zeppelin is a web-based notebook that enables data-driven, interactive data analytics and collaborative documents with SQL, Scala and more.,Apache Zeppelin interpreter concept allows any language/data-processing-backend to be plugged into Zeppelin. Currently Apache Zeppelin supports many interpreters such as Apache Spark, Python, JDBC, Markdown and Shell.,Some basic charts are already included in Apache Zeppelin. Visualizations are not limited to SparkSQL query, any output from any language backend can be recognized and visualized.,Option A is wrong as Hive is data warehouse software facilitates reading, writing, and managing large datasets residing in distributed storage using SQL.,Option B is wrong as D3.js a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.,Option C is wrong as Kinesis Data Analytics is the easiest way to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time.

正确答案: D

正确答案是D，因为Zeppelin提供数据摄取、数据发现、数据分析和数据可视化与协作功能。

请参阅文档 – Zeppelin

Zeppelin是一个基于Web的笔记本，能够实现数据驱动、交互式数据分析和协作文档，支持SQL、Scala等语言。

Apache Zeppelin解释器概念允许将任何语言/数据处理后端插件集成到Zeppelin中。目前，Apache Zeppelin支持多种解释器，如Apache Spark、Python、JDBC、Markdown和Shell。

一些基本的图表已经包含在Apache Zeppelin中。可视化不限于SparkSQL查询，任何语言后端的输出都可以被识别并可视化。

选项A是错误的，因为Hive是数据仓库软件，旨在使用SQL帮助读取、写入和管理存储在分布式存储中的大数据集。

选项B是错误的，因为D3.js是一个JavaScript库，用于基于数据操作文档。D3帮助您使用HTML、SVG和CSS将数据呈现出来。

选项C是错误的，因为Kinesis Data Analytics是分析流数据、获取可操作的洞察力并实时响应您的业务和客户需求的最简单方法。

正确答案: B
Correct answer is B as Redshift Cluster can integrate with corporate HSM via VPN in a cost-effective way,Refer AWS documentation – Redshift Encryption,In Amazon Redshift, you can enable database encryption for your clusters to help protect data at rest. When you enable encryption for a cluster, the data blocks and system metadata are encrypted for the cluster and its snapshots.,You can enable encryption when you launch your cluster, or you can modify an unencrypted cluster to use AWS Key Management Service (AWS KMS) encryption. To do so, you can use either an AWS-managed key or a customer-managed key (CMK). When you modify your cluster to enable KMS encryption, Amazon Redshift automatically migrates your data to a new encrypted cluster. Snapshots created from the encrypted cluster are also encrypted.,Amazon Redshift uses a hierarchy of encryption keys to encrypt the database. You can use either AWS Key Management Service (AWS KMS) or a hardware security module (HSM) to manage the top-level encryption keys in this hierarchy. The process that Amazon Redshift uses for encryption differs depending on how you manage keys. Amazon Redshift automatically integrates with AWS KMS but not with an HSM. When you use an HSM, you must use client and server certificates to configure a trusted connection between Amazon Redshift and your HSM.,Option A is wrong as importing HSM to AWS would not be secure as it against the requirement.,Option C is wrong as Direct Connect would be cheap and quick to start with.,Option D is wrong as CloudHSM cannot connect to on-premises.,Does CloudHSM work with on-premises HSMs? –
Yes. While CloudHSM does not interoperate directly with on-premises HSMs, you can securely transfer exportable keys between CloudHSM and most commercial HSMs using one of several supported RSA key wrap methods.

正确答案: B

正确答案是B，因为Redshift集群可以通过VPN以具有成本效益的方式与企业HSM集成，参考AWS文档 – Redshift加密。在Amazon Redshift中，您可以为集群启用数据库加密，以帮助保护静态数据。当您为集群启用加密时，数据块和系统元数据会被加密，包括集群及其快照。

您可以在启动集群时启用加密，或者可以修改未加密的集群，使用AWS密钥管理服务（AWS KMS）加密。为此，您可以使用AWS管理的密钥或客户管理的密钥（CMK）。当您修改集群以启用KMS加密时，Amazon Redshift会自动将数据迁移到一个新的加密集群。从加密集群创建的快照也会被加密。

Amazon Redshift使用加密密钥层次结构来加密数据库。您可以使用AWS密钥管理服务（AWS KMS）或硬件安全模块（HSM）来管理此层次结构中的顶级加密密钥。Amazon Redshift用于加密的过程取决于您如何管理密钥。Amazon Redshift自动与AWS KMS集成，但不与HSM集成。当使用HSM时，您必须使用客户端和服务器证书来配置Amazon Redshift与您的HSM之间的可信连接。

选项A错误，因为将HSM导入AWS将不安全，违反了要求。

选项C错误，因为Direct Connect开始时会便宜且快速。

选项D错误，因为CloudHSM无法连接到本地HSM。

CloudHSM是否与本地HSM兼容？

是的。虽然CloudHSM不能直接与本地HSM互操作，但您可以使用几种支持的RSA密钥封装方法，在CloudHSM和大多数商业HSM之间安全地传输可导出密钥。

正确答案: C
Correct answer is C as the need is for data at rest encryption. S3 with SSE will help store the data in S3 in encrypted format.,Refer AWS documentation – Redshift Encryption & S3 Encryption,In Amazon Redshift, you can enable database encryption for your clusters to help protect data at rest. When you enable encryption for a cluster, the data blocks and system metadata are encrypted for the cluster and its snapshots.,Encryption is an optional, immutable setting of a cluster. If you want encryption, you enable it during the cluster launch process. To go from an unencrypted cluster to an encrypted cluster or the other way around, unload your data from the existing cluster and reload it in a new cluster with the chosen encryption setting.,Option A is wrong as data is not encrypted in Redshift.,Option B is wrong as data is not encrypted in S3.,Option D is wrong as you cannot enable encryption after Redshift cluster is launched.

正确答案: C

正确答案是C，因为需求是数据静态加密。使用S3与SSE可以帮助将数据以加密格式存储在S3中。

参考AWS文档 – Redshift加密与S3加密

在Amazon Redshift中，您可以为集群启用数据库加密，以帮助保护静态数据。当您为集群启用加密时，数据块和系统元数据会被加密，用于集群及其快照。

加密是集群的可选且不可更改的设置。如果您需要加密，可以在集群启动过程中启用它。要从未加密集群转换为加密集群，或反之，您需要将数据从现有集群卸载并重新加载到新集群中，并选择加密设置。

选项A是错误的，因为Redshift中的数据没有加密。

选项B是错误的，因为S3中的数据没有加密。

选项D是错误的，因为在Redshift集群启动后无法启用加密。

正确答案: D
Correct answer is D as Amazon RDS handles all these requirements, and although Amazon RDS is not typically thought of as optimized for key-value based access, a schema with a good primary key selection can provide this functionality.,Option A is wrong as Amazon S3 provides no fixed schema and does not have consistent read after PUT support.,Option B is wrong as Amazon Kinesis supports streaming data that is consistent as of a given sequence number but doesn’t provide key/value access.,Option C is wrong as Amazon DynamoDB provides key/value access and consistent reads, it does not support SQL-based queries.

正确答案: D

正确答案是D，因为Amazon RDS处理所有这些要求，尽管Amazon RDS通常不被认为是针对键值访问优化的，但一个良好的主键选择的模式可以提供此功能。

选项A是错误的，因为Amazon S3没有固定模式，并且不支持PUT后的持续读取。

选项B是错误的，因为Amazon Kinesis支持基于给定序列号的一致流数据，但不提供键值访问。

选项C是错误的，因为Amazon DynamoDB提供键值访问和一致性读取，但不支持基于SQL的查询。

正确答案: D, E
Correct answers are D & E,Option D as S3 Transfer Acceleration helps speed up the upload performance.
Amazon S3 Transfer Acceleration enables fast, easy, and secure transfers of files over long distances between your client and an S3 bucket. Transfer Acceleration takes advantage of Amazon CloudFront’s globally distributed edge locations. As the data arrives at an edge location, data is routed to Amazon S3 over an optimized network path.,Option E as multipart upload helps provide better recoverability.,Depending on the size of the data you are uploading, Amazon S3 offers the following options:,We recommend that you use multipart uploading in the following ways:,Option A is wrong as the mobile application needs to be configured for different endpoints and does not improve performance. Also, cross region replication would create duplication and increase cost.,Option B is wrong as random prefixes are no more needed for improving performance.,Option C is wrong as Route 53 latency based routing works only with S3 static website.

正确答案: D, E

正确答案是D和E，选项D因为S3 Transfer Acceleration有助于加速上传性能。

Amazon S3 Transfer Acceleration使文件在长距离之间的客户端和S3存储桶之间进行快速、简便且安全的传输。Transfer Acceleration利用了Amazon CloudFront全球分布的边缘位置。当数据到达边缘位置时，数据将通过优化的网络路径传输到Amazon S3。

选项E因为多部分上传有助于提供更好的恢复能力。

根据上传数据的大小，Amazon S3提供以下选项：

我们建议您按照以下方式使用多部分上传：

选项A是错误的，因为移动应用程序需要为不同的端点进行配置，并且无法改善性能。此外，跨区域复制会创建重复并增加成本。

选项B是错误的，因为不再需要随机前缀来改善性能。

选项C是错误的，因为Route 53基于延迟的路由仅适用于S3静态网站。

正确答案: A
Correct answer is A as the data can be encrypted using client side encryption. The encryption needs to be done on the producer before the data is pushed to Kinesis Streams.,Refer AWS documentation – Kinesis Encrypt and Decrypt Data,Options B & D are wrong as they do not provide encryption.,Option C is wrong as the encryption needs to happen at the producer side.

正确答案: A

正确答案是 A，因为数据可以使用客户端加密进行加密。加密需要在数据推送到 Kinesis 流之前在生产者端完成。参考 AWS 文档 – Kinesis 加密和解密数据。

选项 B 和 D 是错误的，因为它们不提供加密。

选项 C 是错误的，因为加密需要在生产者端进行。

正确答案: B
Correct answer is B as EMRFS Consistent View helps provide a view of the objects in S3 and also tracks the consistency.,Refer AWS documentation – EMRFS Consistent View,EMRFS consistent view is an optional feature available when using Amazon EMR release version 3.2.1 or later. Consistent view allows EMR clusters to check for list and read-after-write consistency for Amazon S3 objects written by or synced with EMRFS. Consistent view addresses an issue that can arise due to the Amazon S3 Data Consistency Model. For example, if you add objects to Amazon S3 in one operation and then immediately list objects in a subsequent operation, the list and the set of objects processed may be incomplete. This is more commonly a problem for clusters that run quick, sequential steps using Amazon S3 as a data store, such as multi-step extract-transform-load (ETL) data processing pipelines.,When you create a cluster with consistent view enabled, Amazon EMR uses an Amazon DynamoDB database to store object metadata and track consistency with Amazon S3. If consistent view determines that Amazon S3 is inconsistent during a file system operation, it retries that operation according to rules that you can define.,With consistent view enabled, EMRFS returns the set of objects listed in an EMRFS metadata store and those returned directly by Amazon S3 for a given path. Because Amazon S3 is still the “source of truth” for the objects in a path, EMRFS ensures that everything in a specified Amazon S3 path is being processed regardless of whether it is tracked in the metadata. However, EMRFS consistent view only ensures that the objects in the folders that you track are checked for consistency.

正确答案: B

正确答案是 B，因为 EMRFS 一致视图有助于提供 S3 中对象的视图，并且跟踪一致性。参考 AWS 文档 – EMRFS 一致视图。

EMRFS 一致视图是一个可选功能，适用于使用 Amazon EMR 版本 3.2.1 或更高版本。 一致视图允许 EMR 集群检查由 EMRFS 写入或与之同步的 Amazon S3 对象的列表和写后读取一致性。一致视图解决了由于 Amazon S3 数据一致性模型可能引发的问题。例如，如果你在一次操作中将对象添加到 Amazon S3，然后立即在随后的操作中列出对象，那么列出的对象和处理的对象集合可能不完整。这在使用 Amazon S3 作为数据存储的集群中尤其常见，尤其是快速、连续的步骤，例如多步骤的提取-转换-加载（ETL）数据处理管道。

当你创建一个启用了“一致视图”的集群时，Amazon EMR 使用 Amazon DynamoDB 数据库来存储对象元数据并跟踪与 Amazon S3 的一致性。如果一致视图确定在文件系统操作期间 Amazon S3 不一致，它会根据你定义的规则重试该操作。

启用一致视图时，EMRFS 返回在 EMRFS 元数据存储中列出的对象集合和 Amazon S3 为给定路径直接返回的对象。因为 Amazon S3 仍然是该路径中对象的“真实来源”，EMRFS 确保路径中指定的所有内容都在处理过程中，不管它是否在元数据中被跟踪。然而，EMRFS 一致视图仅确保检查你跟踪的文件夹中的对象一致性。

正确答案: D
Correct answer is D as you can create a view in Redshift which filters the records based on the current user name and show only those results for the logged in user.,Option A is wrong as IAM group with users cannot limit the access to the data in Redshift.,Option B is wrong as it does not limit or filter the data and it is not a cost-effective solution.,Option C is wrong as encryption cannot be done at each employee record level.

正确答案: D

正确答案是 D，因为你可以在 Redshift 中创建一个视图，该视图基于当前用户名过滤记录，只显示已登录用户的结果。

选项 A 错误，因为 IAM 组中的用户无法限制对 Redshift 中数据的访问。

选项 B 错误，因为它不能限制或过滤数据，并且不是一种具有成本效益的解决方案。

选项 C 错误，因为加密不能在每个员工记录级别进行。

正确答案: A
Correct answer is A as Elasticsearch provides full text search capability and is a fully managed AWS service.,Refer AWS documentation – Elasticsearch Integration,You can load streaming data into your Amazon Elasticsearch Service domain from many different sources. Some sources, like Amazon Kinesis Data Firehose and Amazon CloudWatch Logs, have built-in support for Amazon ES. Others, like Amazon S3, Amazon Kinesis Data Streams, and Amazon DynamoDB, use AWS Lambda functions as event handlers. The Lambda functions respond to new data by processing it and streaming it to your domain.,Option B is wrong as Elasticache do not provide search capability, but more of a key value lookup and caching ability.,Option C is wrong as its not easy and cost-efficient to search on DynamoDB,Option D is wrong as S3 does not provide any search capability.

正确答案: A

正确答案是A，因为Elasticsearch提供了全文搜索功能，并且是一个完全托管的AWS服务。

参考AWS文档 – Elasticsearch集成

您可以将流数据从多个不同的来源加载到您的Amazon Elasticsearch Service域中。一些来源，如Amazon Kinesis Data Firehose和Amazon CloudWatch Logs，已经内建对Amazon ES的支持。其他来源，如Amazon S3、Amazon Kinesis Data Streams和Amazon DynamoDB，使用AWS Lambda函数作为事件处理程序。Lambda函数通过处理新数据并将其流式传输到您的域来响应数据。

选项B是错误的，因为Elasticache不提供搜索功能，而是更多提供键值查找和缓存能力。

选项C是错误的，因为在DynamoDB上进行搜索既不容易也不具成本效益。

选项D是错误的，因为S3不提供任何搜索功能。

正确答案: B
Correct answer is B as the key requirement is to reduce cost without affecting quality. The issue here is the JSON data is being read always even though it is not needed 90% of time. As the data size for JSON is huge compared to other attributes, the provisioned throughput needed is high. The issue can be resolved by retrieving only the details for history and the transaction details when needed. Creating a GSI for the base transaction history and using the primary for transaction summary would work perfectly.,Option A is wrong as it would affect the quality.,Option C is wrong as it is not possible.,Option D is wrong as the LSI needs to be created with the sort key as transaction id instead of date. This will allow to retrieve the transaction details from LSI and summary from primary table.,Also, note, both options B & D need changes to the base table as you cannot change the primary key of the table once created and nor can you create a LSI after the table is created.

正确答案: B

正确答案是B，因为关键要求是降低成本而不影响质量。这里的问题是JSON数据总是被读取，即使90%的时间不需要它。由于与其他属性相比，JSON数据的大小巨大，因此所需的预配置吞吐量较高。这个问题可以通过仅在需要时检索历史记录和交易详情来解决。为基础交易历史创建GSI，并使用主键进行交易摘要会完全有效。

选项A是错误的，因为它会影响质量。

选项C是错误的，因为这是不可能的。

选项D是错误的，因为LSI需要使用交易ID作为排序键而不是日期。这样可以从LSI中检索交易详情，从主表中获取摘要。

另外，请注意，B和D选项都需要对基础表进行更改，因为一旦创建了表，无法更改主键，也无法在表创建后创建LSI。

正确答案: D
Correct answer is D as
NOLOAD checks the integrity of all of the data without loading it into the database. The NOLOAD option displays any errors that would occur if you had attempted to load the data. All other options will require subsequent processing on the cluster which will consume resources.,Refer AWS documentation – Data Load Copy Parameters,If you want to validate your data without actually loading the table, use the NOLOAD option with the COPY command.

正确答案: D

正确答案是D，因为 NOLOAD 检查所有数据的完整性，而无需将其加载到数据库中。NOLOAD 选项会显示在尝试加载数据时可能出现的任何错误。所有其他选项都需要在集群上进行后续处理，这将消耗资源。

参考 AWS 文档 – 数据加载复制参数

如果你希望验证数据而不实际加载表格，请使用 COPY 命令中的 NOLOAD 选项。

正确答案: A
Correct answer is A as Kinesis Data Streams can help handle the streaming data. Kinesis Streams provides you with the ability to build custom applications to process and analyze streaming data using KCL which can be used for anomaly detection and processing.,Refer AWS documentation – Kinesis Streams,Although you can use Kinesis Data Streams to solve a variety of streaming data problems, a common use is the real-time aggregation of data followed by loading the aggregate data into a data warehouse or map-reduce cluster.,Data is put into Kinesis data streams, which ensures durability and elasticity. The delay between the time a record is put into the stream and the time it can be retrieved (put-to-get delay) is typically less than 1 second. In other words, a Kinesis Data Streams application can start consuming the data from the stream almost immediately after the data is added. The managed service aspect of Kinesis Data Streams relieves you of the operational burden of creating and running a data intake pipeline. You can create streaming map-reduce–type applications. The elasticity of Kinesis Data Streams enables you to scale the stream up or down, so that you never lose data records before they expire.,Multiple Kinesis Data Streams applications can consume data from a stream, so that multiple actions, like archiving and processing, can take place concurrently and independently. For example, two applications can read data from the same stream. The first application calculates running aggregates and updates an Amazon DynamoDB table, and the second application compresses and archives data to a data store like Amazon Simple Storage Service (Amazon S3). The DynamoDB table with running aggregates is then read by a dashboard for up-to-the-minute reports.,The Kinesis Client Library enables fault-tolerant consumption of data from streams and provides scaling support for Kinesis Data Streams applications.,Option B is wrong as S3 with batching would not provide near real time analysis on the stream data.,Option C is wrong as Kinesis Firehose would only transfer the data to S3. It does not provide ability for KCL applications to work on the streaming data.,Option D is wrong as RDS is not ideal for real time streaming data.

正确答案: A

正确答案是 A，因为 Kinesis 数据流可以帮助处理流数据。Kinesis Streams 提供了构建自定义应用程序的能力，使用 KCL 处理和分析流数据，这可以用于异常检测和处理。参考 AWS 文档 – Kinesis Streams。

尽管你可以使用 Kinesis 数据流解决各种流数据问题，一个常见的用法是对数据进行实时聚合，然后将聚合数据加载到数据仓库或 MapReduce 集群中。

数据被放入 Kinesis 数据流中，从而确保了持久性和弹性。从将记录放入流中到可以检索记录的时间（放入到获取延迟）通常小于 1 秒。换句话说，Kinesis 数据流应用程序几乎可以在数据添加后立即开始从流中消费数据。Kinesis 数据流的托管服务部分解除了你创建和运行数据输入管道的操作负担。你可以创建流式 MapReduce 类型的应用程序。Kinesis 数据流的弹性使你能够根据需要扩展或缩减流量，因此在数据记录过期之前，你不会丢失数据。

多个 Kinesis 数据流应用程序可以从一个流中消费数据，这样就可以并发且独立地执行多个操作，如归档和处理。例如，两个应用程序可以从同一个流中读取数据。第一个应用程序计算运行中的聚合并更新 Amazon DynamoDB 表，第二个应用程序将数据压缩并归档到像 Amazon Simple Storage Service（Amazon S3）这样的数据存储中。然后，包含运行中聚合的 DynamoDB 表被仪表板读取，用于生成最新的报告。

Kinesis 客户端库支持从流中容错地消费数据，并为 Kinesis 数据流应用程序提供扩展支持。

选项 B 是错误的，因为使用批处理的 S3 不提供对流数据的近实时分析。

选项 C 是错误的，因为 Kinesis Firehose 只会将数据传输到 S3。它不提供 KCL 应用程序对流数据进行操作的能力。

选项 D 是错误的，因为 RDS 不适合实时流数据。

正确答案: D
Correct answer is D as Kinesis support Server Side Encryption with which the data can be encrypted at rest.,Refer AWS documentation – Kinesis Server Side Encryption,Server-side encryption is a feature in Amazon Kinesis Data Streams that automatically encrypts data before it’s at rest by using an AWS KMS customer master key (CMK) you specify. Data is encrypted before it’s written to the Kinesis stream storage layer, and decrypted after it’s retrieved from storage. As a result, your data is encrypted at rest within the Kinesis Data Streams service. This allows you to meet strict regulatory requirements and enhance the security of your data.,With server-side encryption, your Kinesis stream producers and consumers don’t need to manage master keys or cryptographic operations. Your data is automatically encrypted as it enters and leaves the Kinesis Data Streams service, so your data at rest is encrypted. AWS KMS provides all the master keys that are used by the server-side encryption feature. AWS KMS makes it easy to use a CMK for Kinesis that is managed by AWS, a user-specified AWS KMS CMK, or a master key imported into the AWS KMS service.,Option A is wrong as SSL/TLS is for Encryption for data in transit,Option B is wrong as Kinesis consumer library is for consumption of data.,Option C is wrong as the data is not encrypted when stored

正确答案: D

正确答案是 D，因为 Kinesis 支持服务器端加密，通过该功能可以对静态数据进行加密。参考 AWS 文档 – Kinesis 服务器端加密。

服务器端加密是 Amazon Kinesis 数据流中的一项功能，它通过使用您指定的 AWS KMS 客户主密钥（CMK）自动加密数据，使数据在静态时保持加密。数据在写入 Kinesis 流存储层之前就会被加密，取出存储后则会被解密。因此，您的数据在 Kinesis 数据流服务中处于静态加密状态。这使您能够满足严格的合规要求并增强数据的安全性。

通过服务器端加密，您的 Kinesis 流生产者和消费者无需管理主密钥或加密操作。数据在进入和离开 Kinesis 数据流服务时会自动加密，因此您的静态数据是加密的。AWS KMS 提供所有由服务器端加密功能使用的主密钥。AWS KMS 使得使用 AWS 管理的 CMK、用户指定的 AWS KMS CMK 或导入到 AWS KMS 服务中的主密钥变得简单。

选项 A 错误，因为 SSL/TLS 用于传输中的数据加密。

选项 B 错误，因为 Kinesis 消费者库用于数据消费。

选项 C 错误，因为数据在存储时未加密。

正确答案: D
Correct answer is D as IP-based resource policy can restrict access to the specific IP addresses only.,Refer AWS documentation – Elasticsearch Access Control,IP-based Policies – IP-based policies restrict access to a domain to one or more IP addresses or CIDR blocks. Technically, IP-based policies are not a distinct type of policy. Instead, they are just resource-based policies that specify an anonymous principal and include a special Condition element.,The primary appeal of IP-based policies is that they allow unsigned requests to an Amazon ES domain, which lets you use clients like curl and Kibana or access the domain through a proxy server.,Options A & B are user/password and ssh keys do not apply to Elasticsearch cluster.,Options C is wrong as you can define identity based policies, however it would not limit access to the specific IP.

正确答案: D

正确答案是 D，因为基于 IP 的资源策略仅能限制对特定 IP 地址的访问。请参考 AWS 文档 – Elasticsearch 访问控制，基于 IP 的策略 – 基于 IP 的策略将访问限制到一个或多个 IP 地址或 CIDR 块。从技术上讲，基于 IP 的策略并不是一种独立的策略类型。相反，它们只是资源基于的策略，指定一个匿名主体并包括一个特殊的条件元素。

基于 IP 的策略的主要吸引力在于它们允许对 Amazon ES 域进行未签名的请求，这使得您可以使用像 curl 和 Kibana 这样的客户端，或者通过代理服务器访问该域。

A 和 B 选项是用户/密码和 SSH 密钥，不适用于 Elasticsearch 集群。

C 选项错误，因为您可以定义基于身份的策略，但它不会限制对特定 IP 的访问。

正确答案: D
Correct answer is D as different CloudWatch log groups can be created, which can have separate access control policies.,Refer AWS documentation – CloudWatch Log Groups,A log group is a group of log streams that share the same retention, monitoring, and access control settings. You can define log groups and specify which streams to put into each group. There is no limit on the number of log streams that can belong to one log group.,Option A is wrong as the logs file are still combined and can be accessed by anyone who has access.,Option B is wrong as its an overhead to create a Lambda function.,Option C is wrong as this would need application changes.

正确答案: D

正确答案是D，因为可以创建不同的CloudWatch日志组，并且这些日志组可以拥有独立的访问控制策略。参考AWS文档 – CloudWatch日志组。

日志组是共享相同保留、监控和访问控制设置的日志流的集合。您可以定义日志组并指定将哪些流放入每个组中。一个日志组可以包含任意数量的日志流，没有限制。

选项A是错误的，因为日志文件仍然被合并，并且可以被任何有访问权限的人访问。

选项B是错误的，因为创建Lambda函数是一个额外的开销。

选项C是错误的，因为这将需要对应用程序进行更改。

正确答案: D
Correct answer is D as Presto can help work on Petabytes of data with the interactive ability.,Refer AWS documentation – EMR Presto,Presto is an open-source distributed SQL query engine optimized for low-latency, ad-hoc analysis of data. It supports the ANSI SQL standard, including complex queries, aggregations, joins, and window functions. Presto can process data from multiple data sources including the Hadoop Distributed File System (HDFS) and Amazon S3.,You can quickly and easily create managed Presto clusters from the AWS Management Console, AWS CLI, or the Amazon EMR API. Additionally, you can leverage additional Amazon EMR features, including fast Amazon S3 connectivity, integration with Amazon EC2 Spot instances, choice of a wide variety of Amazon EC2 instances, including the memory optimized instances, and resize commands to easily add or remove instances from your cluster.,Presto uses a custom query execution engine with operators designed to support SQL semantics. Different from Hive/MapReduce, Presto executes queries in memory, pipelined across the network between stages, thus avoiding unnecessary I/O. The pipelined execution model runs multiple stages in parallel and streams data from one stage to the next as it becomes available.,Run interactive queries that directly access data in Amazon S3, save costs using Amazon EC2 Spot instance capacity, use Auto Scaling to dynamically add and remove capacity, and launch long-running or ephemeral clusters to match your workload. You can also add other Hadoop ecosystem applications on your cluster.,Option A is wrong as Oozie is more of a schedular tool.,Options B & C are wrong as they will work with Map Reduce.

正确答案: D

正确答案是D，因为Presto可以通过交互式能力处理PB级的数据。请参考AWS文档 – EMR Presto。

Presto是一个开源的分布式SQL查询引擎，优化用于低延迟、临时数据分析。它支持ANSI SQL标准，包括复杂查询、聚合、连接和窗口函数。Presto可以处理来自多个数据源的数据，包括Hadoop分布式文件系统（HDFS）和Amazon S3。

您可以通过AWS管理控制台、AWS CLI或Amazon EMR API快速轻松地创建托管的Presto集群。此外，您还可以利用Amazon EMR的其他功能，包括快速的Amazon S3连接、与Amazon EC2 Spot实例的集成、选择各种Amazon EC2实例，包括内存优化实例，以及调整大小命令，以轻松地向集群中添加或删除实例。

Presto使用一个自定义的查询执行引擎，带有支持SQL语义的操作符。与Hive/MapReduce不同，Presto在内存中执行查询，跨网络管道传输各个阶段的数据，从而避免了不必要的I/O。管道执行模型并行运行多个阶段，并在数据可用时将数据从一个阶段流向下一个阶段。

运行直接访问Amazon S3中数据的交互式查询，使用Amazon EC2 Spot实例容量节省成本，使用Auto Scaling动态地添加和删除容量，启动长期运行或临时集群以匹配您的工作负载。您还可以在集群中添加其他Hadoop生态系统应用程序。

选项A是错误的，因为Oozie更多的是一个调度工具。

选项B和C是错误的，因为它们将与MapReduce一起工作。

正确答案: A, D
Correct answer are A, D as the RDS security can tightened using SSL connection and encryption.,Refer AWS documentation – RDS Security,Option B is wrong you cannot SSH into an RDS instance and CloudTrail does not track SSH logins.,Option C is wrong as the RDS instance is AWS managed.

正确答案: A, D

正确答案是 A, D，因为可以通过使用 SSL 连接和加密来增强 RDS 安全性。参见 AWS 文档 – RDS 安全性。

选项 B 错误，无法通过 SSH 访问 RDS 实例，并且 CloudTrail 不跟踪 SSH 登录。

选项 C 错误，因为 RDS 实例由 AWS 管理。

正确答案: A
Correct answer is A as these are AWS specific and not accessible directly. AWS provides access to third party audit reports to confirm the same.,Refer AWS documentation – Risk Compliance Whitepaper,AWS and its customers share control over the IT environment, both parties have responsibility for managing the IT environment. AWS’ part in this shared responsibility includes providing its services on a highly secure and controlled platform and providing a wide array of security features customers can use. The customers’ responsibility includes configuring their IT environments in a secure and controlled manner for their purposes. While customers don’t communicate their use and configurations to AWS, AWS does communicate its security and control environment relevant to customers. AWS does this by doing the following:,Option B is wrong as AWS does not allow access to the data center.,Option C is wrong as security guidelines is not specific to DynamoDB and it is pretty much customer controlled.,Option D is wrong as AWS does not share any DynamoDB system architecture design document.

正确答案: A

正确答案是A，因为这些是AWS特有的，不能直接访问。AWS提供了第三方审计报告的访问权限以确认这一点。参阅AWS文档 – 风险合规白皮书。

AWS和其客户共享IT环境的控制权，双方都有责任管理IT环境。AWS在这种共享责任中的角色包括在高度安全和受控的平台上提供其服务，并提供一系列安全功能供客户使用。客户的责任包括以安全和受控的方式配置他们的IT环境，以满足他们的需求。虽然客户不向AWS传达他们的使用和配置情况，但AWS会传达与客户相关的安全和控制环境。AWS通过以下方式做到这一点：

选项B是错误的，因为AWS不允许访问数据中心。

选项C是错误的，因为安全指南不是DynamoDB特有的，而且它基本上由客户控制。

选项D是错误的，因为AWS不会共享任何DynamoDB系统架构设计文档。

正确答案: D
Correct answer is D as IoT Rules Engine can be used to capture data from Sensor and data received from the device can be inserted into DynamoDB.,Refer AWS documentation – AWS IoT Rules,Rules give your devices the ability to interact with AWS services. Rules are analyzed and actions are performed based on the MQTT topic stream. You can use rules to support tasks like these:,Option A is wrong as a device’s
shadow is a JSON document that is used to store and retrieve current state information for a device. The Device Shadow service maintains a shadow for each device you connect to AWS IoT. You can use the shadow to get and set the state of a device over MQTT or HTTP, regardless of whether the device is connected to the Internet. Each device’s shadow is uniquely identified by the name of the corresponding thing.,Option B is wrong as Redshift is a data warehousing solution.,Option C is wrong as while Kinesis could technically be used as an intermediary between different sources, it isn’t a great way to get data into DynamoDB from an IoT device.

正确答案: D

正确答案是 D，因为 IoT 规则引擎可以用来捕获传感器的数据，并且从设备接收到的数据可以插入到 DynamoDB 中。参考 AWS 文档 – AWS IoT 规则，规则赋予你的设备与 AWS 服务互动的能力。规则会被分析，并根据 MQTT 主题流执行相应的操作。你可以使用规则支持如下任务：

选项 A 错误，因为设备的影像是一个 JSON 文档，用于存储和检索设备的当前状态信息。设备影像服务为你连接到 AWS IoT 的每个设备维护一个影像。你可以使用影像通过 MQTT 或 HTTP 获取和设置设备的状态，无论设备是否连接到互联网。每个设备的影像都通过对应事物的名称唯一标识。

选项 B 错误，因为 Redshift 是一个数据仓库解决方案。

选项 C 错误，因为虽然 Kinesis 在技术上可以作为不同来源之间的中介，但它并不是一个理想的方式来从 IoT 设备将数据传输到 DynamoDB。

正确答案: B
Correct answer is B as Lambda reads in batches from Kinesis from a single shard, and hence it might timeout if the batch of records is huge.,Refer AWS documentation – Lambda with Kinesis,You can use an AWS Lambda function to process records in an Amazon Kinesis data stream. With Kinesis, you can collect data from many sources and process them with multiple consumers. Lambda supports standard data stream iterators and HTTP/2 stream consumers.,Lambda reads records from the data stream and invokes your function synchronously with an event that contains stream records. Lambda reads records in batches and invokes your function to process records from the batch.,Your Lambda function is a consumer application for your data stream. It processes one batch of records at a time from each shard.,For standard iterators, Lambda polls each shard in your Kinesis stream for records at a base rate of once per second. When more records are available, Lambda keeps processing batches until it receives a batch that’s smaller than the configured maximum batch size. The function shares read throughput with other consumers of the shard.,Option A is wrong as adding Lambda function does not reduce the batch of records read from Kinesis.,Option C is wrong as ignoring the data would lead to data loss and does not solve the problem,Option D is wrong as Lambda function reads from a single Kinesis shard.

正确答案: B

正确答案是 B，因为 Lambda 从 Kinesis 中的单个分片按批次读取数据，如果记录批次太大，可能会超时。参考 AWS 文档 – Lambda 与 Kinesis。

您可以使用 AWS Lambda 函数来处理 Amazon Kinesis 数据流中的记录。通过 Kinesis，您可以从多个来源收集数据并通过多个消费者进行处理。Lambda 支持标准数据流迭代器和 HTTP/2 流消费者。

Lambda 从数据流中读取记录，并通过包含流记录的事件同步调用您的函数。Lambda 按批次读取记录，并调用您的函数处理批次中的记录。

您的 Lambda 函数是数据流的消费者应用程序。它一次处理每个分片中的一批记录。

对于标准迭代器，Lambda 以每秒一次的基本速率轮询您的 Kinesis 流中的每个分片。当有更多记录时，Lambda 会继续处理批次，直到收到一个小于配置的最大批次大小的批次。该函数与其他消费者共享分片的读取吞吐量。

选项 A 错误，因为添加 Lambda 函数不会减少从 Kinesis 中读取的记录批次。

选项 C 错误，因为忽略数据会导致数据丢失，并不能解决问题。

选项 D 错误，因为 Lambda 函数是从单个 Kinesis 分片中读取数据。

正确答案: D
Correct answer is D as Kinesis support Server Side Encryption with which the data can be encrypted at rest.,Refer AWS documentation – Kinesis Server Side Encryption,Server-side encryption is a feature in Amazon Kinesis Data Streams that automatically encrypts data before it’s at rest by using an AWS KMS customer master key (CMK) you specify. Data is encrypted before it’s written to the Kinesis stream storage layer, and decrypted after it’s retrieved from storage. As a result, your data is encrypted at rest within the Kinesis Data Streams service. This allows you to meet strict regulatory requirements and enhance the security of your data.,With server-side encryption, your Kinesis stream producers and consumers don’t need to manage master keys or cryptographic operations. Your data is automatically encrypted as it enters and leaves the Kinesis Data Streams service, so your data at rest is encrypted. AWS KMS provides all the master keys that are used by the server-side encryption feature. AWS KMS makes it easy to use a CMK for Kinesis that is managed by AWS, a user-specified AWS KMS CMK, or a master key imported into the AWS KMS service.,Option A is wrong as Kinesis supports encryption at rest,Option B is wrong as SSL/TLS is for Encryption for data in transit,Option C is wrong as S3 SSE does not work with Kinesis

正确答案: D

正确答案是D，因为Kinesis支持服务器端加密，可以在数据静止时进行加密。参考AWS文档 – Kinesis服务器端加密。

服务器端加密是Amazon Kinesis数据流中的一项功能，它会在数据静止之前自动加密数据，使用您指定的AWS KMS客户主密钥（CMK）。数据在写入Kinesis流存储层之前会被加密，从存储中检索后再解密。因此，您的数据在Kinesis数据流服务内静止时是加密的。这使得您能够满足严格的合规要求并增强数据的安全性。

使用服务器端加密时，您的Kinesis流生产者和消费者无需管理主密钥或加密操作。您的数据在进入和离开Kinesis数据流服务时会自动加密，因此您的静态数据是加密的。AWS KMS提供了所有用于服务器端加密功能的主密钥。AWS KMS使得使用由AWS管理的CMK、用户指定的AWS KMS CMK或导入到AWS KMS服务中的主密钥变得更加容易。

选项A是错误的，因为Kinesis支持静态数据加密。

选项B是错误的，因为SSL/TLS用于传输中的数据加密。

选项C是错误的，因为S3 SSE不能与Kinesis一起使用。

正确答案: C
Correct answer is C as the time series data regarding the usage of the customer can give insights and help build and train the model,Refer AWS documentation – AWS Machine Learning Churn prediction,Options A & D are wrong as they do not give any idea of customer behaviour.,Option B is wrong as the data is too limited.

正确答案: C

正确答案是C，因为关于客户使用情况的时间序列数据可以提供洞察力，并帮助构建和训练模型，参考AWS文档 – AWS机器学习流失预测。

A和D选项是错误的，因为它们没有提供任何关于客户行为的线索。

B选项是错误的，因为数据过于有限。

正确答案: D
Correct answer is D as Apache Spark is a distributed processing framework and programming model that helps you do machine learning, stream processing, or graph analytics using Amazon EMR clusters. Similar to Apache Hadoop, Spark is an open-source,distributed processing system commonly used for big data workloads. However, Spark has several notable differences from Hadoop MapReduce. Spark has an optimized directed acyclic graph (DAG) execution engine and actively caches data in-memory, which can boost performance, especially for certain algorithms and interactive queries.,Option A is wrong as Hive is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster. Hive scripts use an SQL-like language called Hive QL (query language) that abstracts programming models and supports typical data warehouse interactions. Hive enables you to avoid the complexities of writing Tez jobs based on directed acyclic graphs (DAGs) or MapReduce programs in a lower level computer language, such as Java. Hive extends the SQL paradigm by including serialization formats.,Option B is wrong as HBase is an open source, non-relational, distributed database developed as part of the Apache Software Foundation’s Hadoop project. HBase runs on top of Hadoop Distributed File System (HDFS) to provide non- relational database capabilities for the Hadoop ecosystem. HBase works seamlessly with Hadoop, sharing its file system and serving as a direct input and output to the MapReduce framework and execution engine. HBase also integrates with Apache Hive, enabling SQL-like queries over HBase tables, joins with Hive-based tables, and support for Java Database Connectivity (JDBC).,Option C is wrong as HCatalog is a tool that allows you to access Hive metastore tables within Pig, Spark SQL, and/or custom MapReduce applications. HCatalog has a REST interface and command line client that allows you to create tables or do other operations.

正确答案: D

正确答案是D，因为Apache Spark是一个分布式处理框架和编程模型，帮助你使用Amazon EMR集群进行机器学习、流处理或图分析。与Apache Hadoop类似，Spark是一个开源的分布式处理系统，通常用于大数据工作负载。然而，Spark与Hadoop MapReduce有几个显著的区别。Spark具有优化的有向无环图（DAG）执行引擎，并且积极地将数据缓存到内存中，这可以提升性能，尤其是在某些算法和交互式查询中。

选项A是错误的，因为Hive是一个开源的数据仓库和分析包，运行在Hadoop集群之上。Hive脚本使用类似SQL的语言，称为Hive QL（查询语言），它抽象了编程模型，并支持典型的数据仓库交互。Hive使你能够避免编写基于有向无环图（DAG）或MapReduce程序的Tez作业的复杂性，Tez作业使用较低级别的计算机语言，如Java。Hive通过包括序列化格式来扩展SQL范式。

选项B是错误的，因为HBase是一个开源的非关系型分布式数据库，作为Apache Software Foundation的Hadoop项目的一部分进行开发。HBase运行在Hadoop分布式文件系统（HDFS）之上，为Hadoop生态系统提供非关系型数据库功能。HBase与Hadoop无缝协作，共享其文件系统，并作为MapReduce框架和执行引擎的直接输入和输出。HBase还与Apache Hive集成，支持对HBase表进行类似SQL的查询、与基于Hive的表进行联接，并支持Java数据库连接（JDBC）。

选项C是错误的，因为HCatalog是一个工具，允许你在Pig、Spark SQL和/或自定义MapReduce应用程序中访问Hive元存储表。HCatalog具有REST接口和命令行客户端，允许你创建表或执行其他操作。

正确答案: B
Key point here hybrid work flow with both automated and manual tasks and ability to replay also needing GPUs with CUD instances with low latency networking,Correct answer is B as SWF provides an ability to have both human and automated assessments with G2 instances in a placement group providing GPU and low latency networking.,Option A & D are wrong as it involves hybrid approach involving human assessments.,Option C & D are wrong as C3 and SR-IOV won’t provide GPU as well as Enhanced networking needs to be enabled

正确答案: B

关键点在于混合工作流，包含自动化和手动任务，并且需要回放功能，同时还需要使用带有低延迟网络的CUD实例的GPU。正确答案是B，因为SWF提供了同时进行人工和自动化评估的能力，且G2实例在放置组中提供GPU和低延迟网络。

A选项和D选项错误，因为它们涉及了包含人工评估的混合方法。

C选项和D选项错误，因为C3和SR-IOV无法提供GPU，并且增强网络需要被启用。

正确答案: D
Correct answer is D as only the logs need to be maintained in the new country, S3 cross region replication can be used to copy the data to the AWS region within the new Country.,Option A is wrong as there is not need for replication the complete application,Option B is wrong as CloudFront logs would only provide access logs and maybe not hold the financial transaction logs.,Option C is wrong as using Kinesis would need to build and host client application and have data storage charges as well.

正确答案: D

正确答案是D，因为只需要在新国家维护日志，可以使用S3跨区域复制将数据复制到新国家的AWS区域。选项A错误，因为不需要复制完整的应用程序。选项B错误，因为CloudFront日志只提供访问日志，可能不包含财务交易日志。选项C错误，因为使用Kinesis需要构建和托管客户端应用程序，并且还会产生数据存储费用。

正确答案: B
Key point here is backend supporting the data with 2 years retention and architecture being scalable,Correct answer is B as DynamoDB can be used to support the ingestion throughput via autoscaled instances and later store data into Redshift for analysis,Option A & D are wrong as RDS would not be scalable and performant with high input rate and storage for 2 years,Option C is wrong as Redshift is designed for data warehousing and would not be able to support the ingestion throughput

正确答案: B

这里的关键点是后端支持两年数据保留并且架构可扩展，正确答案是B，因为DynamoDB可以通过自动扩展的实例支持数据摄取吞吐量，并将数据存储到Redshift中进行分析，选项A和D是错误的，因为RDS在面对高输入速率和两年存储时无法提供可扩展性和性能，选项C是错误的，因为Redshift是为数据仓库设计的，无法支持数据摄取吞吐量。

正确答案: D
Correct answer is D as the data dependency can be managed in DynamoDB. S3 event notifications can trigger Lambda functions to map the objects and check dependency. Once all satisfied, EMR job can be triggered.,Option A is wrong as EMR hive with RDS with need resource running always and not a cost-effective solution.,Option B is wrong as using EC2 fleet of servers would not be cost-effective as compared to lambda.,Option C is wrong as ElastiCache Redis is not an ideal storage for mapping and would not be cost-effective as compared to DynamoDB being a completely managed service.

正确答案: D

正确答案是 D，因为数据依赖可以在 DynamoDB 中管理。S3 事件通知可以触发 Lambda 函数来映射对象并检查依赖关系。一旦所有条件满足，就可以触发 EMR 作业。

选项 A 错误，因为 EMR hive 与 RDS 需要持续运行资源，这不是一种具有成本效益的解决方案。

选项 B 错误，因为与 Lambda 相比，使用 EC2 服务器集群的成本效益较低。

选项 C 错误，因为 ElastiCache Redis 不是理想的映射存储，相比于 DynamoDB（完全托管的服务），它的成本效益较低。

正确答案: C
Correct answer is C as the shards are more than enough and the EC2 instances utilization is low, the only other reason can be the instances do not match up the shards and single instance is processing multiple shards.,Refer AWS documentation – Kinesis Record Processor Scaling,Typically, when you use the KCL, you should ensure that the number of instances does not exceed the number of shards (except for failure standby purposes). Each shard is processed by exactly one KCL worker and has exactly one corresponding record processor, so you never need multiple instances to process one shard. However, one worker can process any number of shards, so it’s fine if the number of shards exceeds the number of instances.,To scale up processing in your application, you should test a combination of these approaches:,Note that you can use Auto Scaling to automatically scale your instances based on appropriate metrics.,Option A is wrong as shards are more than enough increasing would not improve performance.,Option B is wrong as the network traffic is modest increasing the size would not improve performance.,Option D is wrong as there is no relation of DynamoDB.

正确答案: C

正确答案是C，因为碎片已经足够多，而EC2实例的利用率较低，唯一的其他原因可能是实例没有与碎片匹配，导致单个实例处理多个碎片。请参考AWS文档 – Kinesis记录处理器扩展。

通常，在使用KCL时，您应确保实例的数量不超过碎片的数量（除非用于故障备用）。每个碎片由一个KCL工作者处理，并且有一个相应的记录处理器，因此您无需多个实例来处理一个碎片。然而，一个工作者可以处理任意数量的碎片，因此如果碎片的数量超过实例的数量也是可以的。

为了扩展应用程序中的处理能力，您应测试这些方法的组合：

请注意，您可以使用自动扩展根据适当的指标自动扩展实例。

选项A是错误的，因为碎片已经足够多，增加并不会提高性能。

选项B是错误的，因为网络流量适中，增加大小不会提高性能。

选项D是错误的，因为DynamoDB与此无关。

正确答案: A, D
Correct answers are A & D as EVEN distribution distributes the data across slides in a round robin fashion and does not participate in joins.,Refer AWS documentation – Redshift Distribution Style,EVEN distribution – The leader node distributes the rows across the slices in a round-robin fashion, regardless of the values in any particular column. EVEN distribution is appropriate when a table does not participate in joins or when there is not a clear choice between KEY distribution and ALL distribution.,Option B is wrong as if the data needs to be grouped by specific Key, KEY distribution should be used.,The rows are distributed according to the values in one column. The leader node places matching values on the same node slice. If you distribute a pair of tables on the joining keys, the leader node collocates the rows on the slices according to the values in the joining columns so that matching values from the common columns are physically stored together.,Option C is wrong as if the data transfer needs to be eliminated, ALL distribution should be used as a copy of all the data is made on all the nodes.,A copy of the entire table is distributed to every node. Where EVEN distribution or KEY distribution place only a portion of a table’s rows on each node, ALL distribution ensures that every row is collocated for every join that the table participates in.

正确答案: A, D

正确答案是 A 和 D，因为 EVEN 分配方式将数据以循环的方式分配到各个分片，并且不参与连接操作。参见 AWS 文档 – Redshift 分配方式。

EVEN 分配 – 领导节点以循环方式将行分配到各个分片，而不考虑任何特定列中的值。当表不参与连接操作时，或者在 KEY 分配和 ALL 分配之间没有明确选择时，EVEN 分配是适合的。

选项 B 错误，因为如果数据需要按特定键进行分组，应使用 KEY 分配。

行是根据某一列中的值进行分配的。领导节点将匹配的值放置在相同的节点分片上。如果您根据连接键分配一对表，领导节点会根据连接列中的值将行在分片上协同分配，以确保来自公共列的匹配值物理上存储在一起。

选项 C 错误，因为如果需要消除数据传输，应该使用 ALL 分配，因为所有节点都会复制一份所有数据。

整个表的副本被分配到每个节点上。与 EVEN 分配或 KEY 分配只将表的一部分行放置在每个节点上不同，ALL 分配确保每个行都在每个表参与连接的情况下被协同分配。

正确答案: A
Correct answer is A as DynamoDB can be used to store the data. Access to fields can be controlled using DynamoDB fine grained access control, which can be mapped to IAM role. This solution also requires the least amount of management effort.,Refer AWS documentation – DynamoDB Control Access,In DynamoDB, you have the option to specify conditions when granting permissions using an IAM policy (see Access Control). For example, you can:,Option B is wrong as S3 would not provide fine grained access control of data within the file.,Option C is wrong as although its possible, the option does not satisfy the least amount of management work requirement.,Option D is wrong as Redshift is more for a data warehouse solution and comes with management effort.

正确答案: A

正确答案是A，因为DynamoDB可以用来存储数据。可以通过DynamoDB的细粒度访问控制来控制对字段的访问，该控制可以映射到IAM角色。这个解决方案还需要最少的管理工作。

参见AWS文档 – DynamoDB控制访问

在DynamoDB中，您可以在使用IAM策略授予权限时指定条件（请参见访问控制）。例如，您可以：

选项B是错误的，因为S3无法提供文件内部数据的细粒度访问控制。

选项C是错误的，尽管可以实现，但该选项不符合最少管理工作量的要求。

选项D是错误的，因为Redshift更多是一个数据仓库解决方案，并且需要管理工作。

正确答案: D
Correct answer is D Composite key with Sensor ID and timestamp would help for faster queries,Refer AWS documentation for DynamoDB handling Timeseries data,Option C & D are valid as they are keeping tables for each week. However, with Option C, concatenation will cause queries would be slowerTable should be designed with a composite primary key consisting of Customer ID as the partition key and date/time as the sort keyOption A & B are wrong as One table would not make sense as we need to query only on past week and want data only for 4 weeks. This would impact performance. Also, provisioned throughput consumption is based on the size of the deleted item and its more costly as compared to dropping a table.

正确答案: D

正确答案是D，使用包含传感器ID和时间戳的复合键可以帮助提高查询速度，参考AWS文档了解DynamoDB如何处理时间序列数据。选项C和D有效，因为它们为每周维护一个表。然而，选项C中的串联会导致查询变慢。

表应设计为使用由客户ID作为分区键和日期/时间作为排序键的复合主键。

选项A和B是错误的，因为一个表在这种情况下没有意义，因为我们只需要查询过去一周的数据，并且只需要4周的数据。这会影响性能。此外，预配置的吞吐量消耗是基于删除项的大小的，且其成本比删除一个表更高。

正确答案: C
Correct answer is C as QuickSight provides visualization capability with integration with RDS, Redshift.,Refer AWS documentation – QuickSight,Amazon QuickSight is a fast, cloud-powered business intelligence service that makes it easy to deliver insights to everyone in your organization.,As a fully managed service, QuickSight lets you easily create and publish interactive dashboards that include ML Insights. Dashboards can then be accessed from any device, and embedded into your applications, portals, and websites.,QuickSight allows you to directly connect to and import data from a wide variety of cloud and on-premises data sources. These include SaaS applications such as Salesforce, Square, ServiceNow, Twitter, Github, and JIRA; 3rd party databases such as Teradata, MySQL, Postgres, and SQL Server; native AWS services such as Redshift, Athena, S3, RDS, and Aurora; and private VPC subnets. You can also upload a variety of file types including Excel, CSV, JSON, and Presto.,Option A is wrong as Hue is a Web interface for analyzing data with Hadoop.,Option B is wrong as Redshift is a fast, scalable data warehouse that makes it simple and cost-effective to analyze all your data across your data warehouse and data lake.,Option D is wrong as Elasticsearch is a fully managed service that makes it easy for you to deploy, secure, and operate Elasticsearch at scale with zero down time. It does not provide visualization support and needs to be used with Kibana and nor does it integrate with RDS, Redshift. The data needs to be loaded into Elasticsearch.

正确答案: C

正确答案是 C，因为 QuickSight 提供了与 RDS、Redshift 的集成和可视化功能。请参考 AWS 文档 – QuickSight。

Amazon QuickSight 是一个快速的、基于云的商业智能服务，能够轻松为组织中的每个人提供洞察。

作为一个完全托管的服务，QuickSight 让您可以轻松创建和发布包含 ML Insights 的互动仪表板。然后，您可以在任何设备上访问仪表板，并将其嵌入到您的应用程序、门户和网站中。

QuickSight 允许您直接连接并导入来自各种云端和本地数据源的数据。这些数据源包括 SaaS 应用程序，如 Salesforce、Square、ServiceNow、Twitter、Github 和 JIRA；第三方数据库，如 Teradata、MySQL、Postgres 和 SQL Server；本地 AWS 服务，如 Redshift、Athena、S3、RDS 和 Aurora；以及私有 VPC 子网。您还可以上传多种文件类型，包括 Excel、CSV、JSON 和 Presto。

选项 A 错误，因为 Hue 是用于分析 Hadoop 数据的 Web 界面。

选项 B 错误，因为 Redshift 是一个快速、可扩展的数据仓库，使得分析所有数据变得简单且具有成本效益，适用于数据仓库和数据湖。

选项 D 错误，因为 Elasticsearch 是一个完全托管的服务，能够轻松部署、保护和操作 Elasticsearch，且不会有停机时间。它不提供可视化支持，且需要与 Kibana 一起使用，也没有与 RDS、Redshift 集成。数据需要加载到 Elasticsearch 中。

正确答案: A, B
Correct answers are A & B,Option A as Mahout provides recommender engine/collaborative filtering capability,Option B as Spark’s MLlib machine learning library should help with this task. Amazon ML is limited to 100 “categorical’ recommendations, so a custom system is required for this purpose.,Option C is wrong as Amazon ML is limited to 100 “categorical’ recommendations, so a custom system is required for this purpose.,Option D is wrong as RDS MySQL is just a database engine and does not provide analytics capability.

正确答案: A, B

正确答案是 A 和 B，选项 A 是因为 Mahout 提供了推荐引擎/协同过滤功能，选项 B 是因为 Spark 的 MLlib 机器学习库应该有助于完成此任务。亚马逊 ML 限制为 100 个“类别”推荐，因此需要一个自定义系统来完成此任务。

选项 C 错误，因为亚马逊 ML 限制为 100 个“类别”推荐，因此需要一个自定义系统来完成此任务。

选项 D 错误，因为 RDS MySQL 只是一个数据库引擎，并不提供分析功能。

正确答案: C
Correct answer is C as the core of this question is how to send event messages to Kinesis synchronously vs. asynchronously. The critical events must be sent synchronously, and the informational events can be sent asynchronously. The Kinesis Producer Library (KPL) implements an asynchronous send function, so it can be used for the informational messages. PutRecords is a synchronous send function, so it must be used for the critical events.,Refer AWS documentation – Developing Producers using KPL,Because the KPL may buffer records before sending them to Kinesis Data Streams, it does not force the caller application to block and wait for a confirmation that the record has arrived at the server before continuing execution. A call to put a record into the KPL always returns immediately and does not wait for the record to be sent or a response to be received from the server. Instead, a Future object is created that receives the result of sending the record to Kinesis Data Streams at a later time. This is the same behavior as asynchronous clients in the AWS SDK.,Option A is wrong as Kinesis Producer Library sends all events asynchronously,Option B is wrong as critical events needs to be synchronous and information events needs to be asynchronous.,Option D is wrong as PutRecords sends all events synchronously

正确答案: C

正确答案是C，因为这个问题的核心是如何同步与异步地将事件消息发送到Kinesis。关键事件必须同步发送，信息性事件可以异步发送。Kinesis生产者库（KPL）实现了一个异步发送功能，因此可以用于信息性消息。PutRecords是一个同步发送功能，因此必须用于关键事件。

参考AWS文档 – 使用KPL开发生产者

由于KPL可能在将记录发送到Kinesis数据流之前对记录进行缓冲，它并不强制调用方应用程序阻塞并等待确认记录已到达服务器后再继续执行。调用KPL的put方法始终会立即返回，并且不会等待记录发送或从服务器接收到响应。相反，它会创建一个Future对象，在稍后时间接收将记录发送到Kinesis数据流的结果。这与AWS SDK中的异步客户端行为相同。

选项A是错误的，因为Kinesis生产者库将所有事件异步发送。

选项B是错误的，因为关键事件需要同步发送，信息事件需要异步发送。

选项D是错误的，因为PutRecords将所有事件同步发送。

正确答案: C
Correct answer is C as Binary classification can be used to predict for whether the transaction is fraudulent or not.

正确答案：C

正确答案是C，因为二元分类可用于预测交易是否欺诈。

正确答案: C
Correct answer is C as Pie charts are best to use when you are trying to compare parts of a whole, which is ideal for the use case. They do not show changes over time.,Refer AWS documentation – QuickSight Chart Types,Option A is wrong as Line graphs are used to track changes over short and long periods of time.,Option B is wrong as Column chart is a data visualization where each category is represented by a rectangle, with the height of the rectangle being proportional to the values being plotted,Option D is wrong as Scatter plot is a two-dimensional data visualization that uses dots to represent the values obtained for two different variables – one plotted along the x-axis and the other plotted along the y-axis

正确答案: C

正确答案是 C，因为饼图最适合用于比较整体的各个部分，这对于该用例是理想的。饼图不显示随时间变化的情况。

参考AWS文档 – QuickSight 图表类型。

选项 A 错误，因为折线图用于跟踪短期和长期的变化。

选项 B 错误，因为柱状图是一种数据可视化方式，其中每个类别由一个矩形表示，矩形的高度与绘制的值成比例。

选项 D 错误，因为散点图是一种二维数据可视化方式，使用点来表示两个不同变量的值——一个绘制在 x 轴上，另一个绘制在 y 轴上。

正确答案: B, D
Correct answers are B & D as the key reason for throttling is hot keys, as the application does not consume the entire provisioned capacity.,Option B as CloudWatch helps shows the stats for consumed vs provisioned throughput capacity.,Option D as an LSI consumes WCU for writes on the primary table.,Refer AWS documentation – Throttled DB & DynamoDB LSI Considerations & DynamoDB CloudWatch,Partitions are usually throttled when they are accessed by your downstream applications much more frequently than other partitions (that is, a “hot” partition), or when workloads rely on short periods of time with high usage (a “burst” of read or write activity).
To avoid hot partitions and throttling, you must optimize your table and partition structure.,Distribute your read operations and write operations as evenly as possible across your table. A “hot” partition can degrade the overall performance of your table.,Write Capacity Units – When an item in a table is added, updated, or deleted, updating the local secondary indexes will consume provisioned write capacity units for the table. The total provisioned throughput cost for a write is the sum of write capacity units consumed by writing to the table and those consumed by updating the local secondary indexes.,You can monitor ConsumedReadCapacityUnits or ConsumedWriteCapacityUnits over the specified time period, to track how much of your provisioned throughput is being used.,Option A is wrong as GSI does not impact primary table throughput capacity.,Option C is wrong as the provisioned capacity is not exceeded, the average would not be of much help.,Option E is wrong as the provisioned capacity is not exceeded the historical stats would not be of much help.

正确答案: B, D

正确答案是 B 和 D，主要原因是热点键，因应用程序并没有消耗整个预配置的容量。选项 B 因为 CloudWatch 显示了已消耗和预配置的吞吐量容量统计信息。选项 D 因为 LSI 在主表上写入时消耗写入容量单位（WCU）。参见 AWS 文档 – 限流的数据库和 DynamoDB LSI 注意事项以及 DynamoDB CloudWatch。

当分区被下游应用程序访问的频率远高于其他分区（即“热点”分区），或工作负载依赖于短时间内的高使用率（即“突发”读写活动）时，分区通常会被限流。

为了避免热点分区和限流，必须优化您的表和分区结构。尽可能均匀地分布读操作和写操作在表中。一个“热点”分区会降低表的整体性能。

写入容量单位 – 当表中的项被添加、更新或删除时，更新本地二级索引将消耗表的预配置写入容量单位。写入的总预配置吞吐量成本是写入表时消耗的写入容量单位与更新本地二级索引时消耗的容量单位之和。

您可以监控指定时间段内的已消耗读容量单位（ConsumedReadCapacityUnits）或已消耗写容量单位（ConsumedWriteCapacityUnits），以跟踪已使用的预配置吞吐量。

选项 A 错误，因为 GSI 不影响主表的吞吐量容量。

选项 C 错误，因为预配置的容量未被超出，平均值帮助不大。

选项 E 错误，因为预配置的容量未被超出，历史统计信息帮助不大。

正确答案: B
Correct answer is B as multiple teams query different columns with different queries it would be best to use Interleaved keys to improve performance. Interleaved keys are provided to help with the limitations of compound keys. They are designed to weigh each column in the key evenly, allowing improved performance regardless of which columns in the key you’re filtering.,Refer AWS documentation – Redshift Interleaved Sort Keys,An interleaved sort gives equal weight to each column, or subset of columns, in the sort key. If multiple queries use different columns for filters, then you can often improve performance for those queries by using an interleaved sort style. When a query uses restrictive predicates on secondary sort columns, interleaved sorting significantly improves query performance as compared to compound sorting.,Options A & C are wrong as they create duplicate copies or refer to the same underlying table and would not improve performance.,Option D is wrong as the key here is queries on same table as Amazon Redshift workload management (WLM) enables users to flexibly manage priorities within workloads so that short, fast-running queries won’t get stuck in queues behind long-running queries.

正确答案: B

正确答案是B，因为多个团队使用不同的查询查询不同的列，最好使用交错键来提高性能。交错键旨在帮助解决复合键的局限性。它们被设计用来平衡键中每一列的权重，从而无论你过滤的是键中的哪一列，都能提高性能。

参考AWS文档 – Redshift交错排序键

交错排序为排序键中的每一列或列的子集赋予相等的权重。如果多个查询使用不同的列进行过滤，则通过使用交错排序样式通常可以提高这些查询的性能。当查询在次排序列上使用限制性谓词时，交错排序相比复合排序能显著提高查询性能。

选项A和C是错误的，因为它们创建了重复的副本或引用相同的基础表，并不能提高性能。

选项D是错误的，因为这里的键是对同一表的查询，而Amazon Redshift工作负载管理（WLM）使用户能够灵活地管理工作负载中的优先级，从而避免短时间运行的查询被长期运行的查询堵塞在队列中。

正确答案: B
Correct answer is B as the UNLOAD ENCRYPTED command automatically stores the data encrypted using-client side encryption and uses HTTPS to encrypt the data during the transfer to S3.,Refer AWS documentation – Redshift Unloading Data,UNLOAD automatically creates files using Amazon S3 server-side encryption with AWS-managed encryption keys (SSE-S3). You can also specify server-side encryption with an AWS Key Management Service key (SSE-KMS) or client-side encryption with a customer-managed key (CSE-CMK).,Option C is wrong because the data would not be encrypted in flight, and you cannot encrypt an entire bucket with a KMS key.,Options A & D are wrong as the most scalable solutions are the UNLOAD/COPY solutions because they will work in parallel.

正确答案: B

正确答案是B，因为UNLOAD ENCRYPTED命令会自动使用客户端加密存储数据，并在传输到S3期间使用HTTPS加密数据。参考AWS文档 – Redshift卸载数据。

UNLOAD会自动使用Amazon S3服务器端加密（SSE-S3）和AWS管理的加密密钥创建文件。你还可以指定使用AWS Key Management Service密钥（SSE-KMS）进行服务器端加密，或者使用客户管理的密钥（CSE-CMK）进行客户端加密。

选项C是错误的，因为数据在传输过程中不会被加密，而且你不能使用KMS密钥加密整个存储桶。

选项A和D是错误的，因为最具可扩展性的解决方案是UNLOAD/COPY解决方案，因为它们可以并行工作。

正确答案: C
Correct answer is C as the data can be ingested into the Kinesis streams using agents and the logs can then be analyzed real time.,Refer AWS documentation – Kinesis Serverless log Analytics,Amazon Kinesis Streams enables you to build custom applications that process or analyze streaming data for specialized needs. Amazon Kinesis Streams can continuously capture and store terabytes of data per hour from hundreds of thousands of sources such as website clickstreams, financial transactions, social media feeds, IT logs, and location-tracking events.,Option A & B are wrong as analyzing the logs every hour does not provide real time capability as required.,Option D is wrong as storing the logs on EC2 instance is not a scalable performant model,Option E is wrong as Amazon Kinesis stream work on the push mechanism, and the data from the EC2 instances need to be ingested into the Kinesis streams.

正确答案: C

正确答案是C，因为数据可以通过代理被摄取到Kinesis流中，然后日志可以实时分析。参考AWS文档 – Kinesis无服务器日志分析。

Amazon Kinesis Streams使您能够构建定制应用程序，以处理或分析流数据，满足特定需求。Amazon Kinesis Streams可以持续捕获并存储每小时从数十万来源（如网站点击流、金融交易、社交媒体信息流、IT日志和位置跟踪事件）中获得的TB级数据。

选项A和B是错误的，因为每小时分析一次日志无法提供所需的实时能力。

选项D是错误的，因为将日志存储在EC2实例上不是一个可扩展的高效模型。

选项E是错误的，因为Amazon Kinesis流是基于推送机制工作的，EC2实例中的数据需要被摄取到Kinesis流中。

正确答案: B
Key point here to architect durable collection platform with real time analytics, data mining storage.,Correct answer is B to use Kinesis to capture the data in a elastic, durable and parallel manner. Analyze data with Kinesis clients and store data to Redshift for data mining using EMR.,Option A is wrong as S3 would not be ideal to capture data with that frequency and daily job will not provide real time analytics,Option C is wrong as SQS is not an ideal solution to capture this data and Kinesis clients are required to analyze the data. SQL server might not be a scalable option,Option D is wrong as EMR alone is not ideal to capture data and would need specific frameworks like Kafka to capture data for processing. Also real time analytics needs to done using Spark Streaming and not EMR alone. DynamoDB is not for data mining.

正确答案: B

关键点在于设计一个具有实时分析、数据挖掘存储功能的持久化数据收集平台。正确答案是 B，使用 Kinesis 以弹性、持久和并行的方式捕获数据。通过 Kinesis 客户端分析数据，并将数据存储到 Redshift 进行数据挖掘，使用 EMR。

选项 A 错误，因为 S3 不适合以那种频率捕获数据，并且每日任务无法提供实时分析。

选项 C 错误，因为 SQS 不是捕获此数据的理想解决方案，并且需要使用 Kinesis 客户端来分析数据。SQL Server 可能不是一个可扩展的选项。

选项 D 错误，因为仅使用 EMR 不适合捕获数据，并且需要像 Kafka 这样的特定框架来捕获数据进行处理。此外，实时分析需要使用 Spark Streaming，而不是仅使用 EMR。DynamoDB 不适合进行数据挖掘。

正确答案: B
Correct answer is B as Presto can help query over multiple datasources and also provides connectors to interact directly MySQL, Redshift and Hive.,Refer AWS documentation – EMR Presto,Presto is an open-source distributed SQL query engine optimized for low-latency, ad-hoc analysis of data. It supports the ANSI SQL standard, including complex queries, aggregations, joins, and window functions. Presto can process data from multiple data sources including the Hadoop Distributed File System (HDFS) and Amazon S3.,Option A is wrong as data is replicated and is not a cost effective solution.,Option C is wrong as Elasticsearch does not provide analytics capabilities.,Option D is wrong as running on EC2 instances is not a scalable and cost-effective solution.

正确答案: B

正确答案是B，因为Presto可以帮助在多个数据源上进行查询，并且还提供与MySQL、Redshift和Hive直接交互的连接器。参考AWS文档 – EMR Presto。

Presto是一个开源的分布式SQL查询引擎，优化了低延迟的即席数据分析。它支持ANSI SQL标准，包括复杂查询、聚合、连接和窗口函数。Presto可以处理来自多个数据源的数据，包括Hadoop分布式文件系统（HDFS）和Amazon S3。

选项A是错误的，因为数据是被复制的，且不是一种具有成本效益的解决方案。

选项C是错误的，因为Elasticsearch不提供分析功能。

选项D是错误的，因为运行在EC2实例上不是一种可扩展且具有成本效益的解决方案。

正确答案: A
Correct answer is A as Bar Chart can be used to represent the data for comparison in sales for each region.,Refer AWS documentation – QuickSight Visual Types,Option B is wrong as Use line charts to compare changes in measure values over period of time,Option C is wrong as Stacked area chart is the extension of a basic area chart to display the evolution of the value of several groups on the same graphic.,Option D is wrong as Histograms are sometimes confused with bar charts. A histogram is used for continuous data, where the bins represent ranges of data, while a bar chart is a plot of categorical variables.

正确答案: A

正确答案是A，因为条形图可以用来表示每个地区销售数据的比较。

请参考AWS文档 – QuickSight可视化类型。

选项B是错误的，因为折线图用于比较一段时间内度量值的变化。

选项C是错误的，因为堆叠面积图是基本面积图的扩展，用于显示多个组在同一图形上的值的演变。

选项D是错误的，因为直方图有时会与条形图混淆。直方图用于表示连续数据，其中箱子代表数据范围，而条形图则是分类变量的图表。

正确答案: B
Correct answer is B as Redshift is a fully managed data warehousing solution providing standard SQL interface and ability to run complex queries.,Refer AWS documentation – Redshift,Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.,Option A is wrong as SQS does not provide querying capability,Option C is wrong as Athena does not provide complex querying capability,Option D is wrong as EMR provides a managed Hadoop framework that makes it easy, fast, and cost-effective to process vast amounts of data across dynamically scalable Amazon EC2 instances.

正确答案: B

正确答案是B，因为Redshift是一个完全托管的数据仓库解决方案，提供标准SQL接口和运行复杂查询的能力。请参阅AWS文档 – Redshift。

Amazon Redshift是一个快速的、完全托管的数据仓库，使您能够使用标准SQL和现有的商业智能（BI）工具，简单且经济高效地分析所有数据。它允许您针对PB级结构化数据运行复杂的分析查询，使用先进的查询优化、列存储在高性能本地磁盘上，并进行大规模并行查询执行。

选项A是错误的，因为SQS不提供查询功能。

选项C是错误的，因为Athena不提供复杂查询能力。

选项D是错误的，因为EMR提供一个托管的Hadoop框架，使得跨动态可扩展的Amazon EC2实例处理大量数据变得容易、快速且经济高效。

正确答案: A, D
Correct answers are A & D as one of the requirement is data security with encryption at rest and in transit. PCI DSS helps ensure that companies maintain a secure environment for storing, processing, and transmitting credit card information.,Option B is wrong as versioning only helps maintain data versions and are helpful to recover from accidental overwrites or deletions.,Option C is wrong as this is not a requirement and a best practice.

正确答案: A, D

正确答案是 A 和 D，因为其中一项要求是数据安全，包括静态加密和传输加密。PCI DSS 有助于确保公司为存储、处理和传输信用卡信息提供一个安全的环境。

选项 B 错误，因为版本控制仅有助于维护数据版本，并且有助于从意外覆盖或删除中恢复。

选项 C 错误，因为这不是一个要求，而是最佳实践。

正确答案: B
Key point here is to optimize cost and performance only for the increased workload only, not the existing one,Refer EMR best practices – For unpredictable workloads, the suggested pricing model is Spot or On-Demand.,Correct answer is B and preferred over A as DynamoDB would be preferred over RDS for the throughput supported and Spot instances to reduce cost and handle the temporary workload.,Option C & D are wrong as Reserved instances would be preferred for a consistent and predictable workload and would prove costly in this scenario.

正确答案: B

关键点在于仅针对增加的工作负载优化成本和性能，而不是现有的工作负载。参考 EMR 最佳实践 – 对于不可预测的工作负载，建议的定价模型是 Spot 或按需定价。

正确答案是 B，优于 A，因为 DynamoDB 比 RDS 更适合支持吞吐量，且 Spot 实例有助于降低成本并处理临时工作负载。

选项 C 和 D 错误，因为预留实例适用于一致和可预测的工作负载，而在这种情况下会导致高成本。

正确答案: A
Correct answer is A as the S3 with SSE provides encryption at rest and HTTPS can be used to push data to S3 for encryption in transit. S3 provides an option for cost effective long term storage. Ephemeral drives would help run simulations and the data would lost once the EC2 instance is terminated.,Option B is wrong as S3 with SSE provides encryption at rest and HTTPS can be used to push data to S3 for encryption in transit. However, in memory simulations with 5 TB data would not be feasible.,Option C & D are wrong as HDFS is not an cost effective solution as data nodes would be required to store the data and it does not provide encryption by default.,Option E is wrong as EBS for long term storage is an expensive option.

正确答案: A

正确答案是A，因为启用SSE的S3提供静态加密，并且可以使用HTTPS将数据推送到S3进行传输加密。S3提供了一个成本效益高的长期存储选项。临时驱动器有助于运行模拟，一旦EC2实例终止，数据将丢失。

选项B错误，因为启用SSE的S3提供静态加密，并且可以使用HTTPS将数据推送到S3进行传输加密。然而，在内存中进行5TB数据的模拟是不可行的。

选项C和D错误，因为HDFS不是一个成本效益高的解决方案，因为需要数据节点来存储数据，并且默认情况下不提供加密。

选项E错误，因为将EBS用于长期存储是一种昂贵的选择。

正确答案: C
Correct answer is C as the EMR jobs needs to process daily data, it would be best to partition the data by day.,Refer AWS documentation – EMR Best Practices,Data partitioning is an essential optimization to your data processing workflow. Without any data partitioning in place, your data processing job needs to read or scan all available data sets and apply additional filters in order to skip unnecessary data. Such architecture might work for a low volume of data, but scanning the entire data set is a very time consuming and expensive approach for larger data sets. Data partitioning lets you create unique buckets of data and eliminate the need for a data processing job to read the entire data set.,Three considerations determine how you partition your data:,For instance, if you are processing a time-series data set where you need to process your data once every hour and your data-access pattern is based on time, partitioning your data based on date makes the most sense. An example of such data processing would be processing your daily logs. If you have incoming logs from variety of data sources (web servers, devices etc.), then creating partitions of data based on the hour of the day gives you a date-based partitioning scheme.,The structure of such partitioning scheme will look similar to the following:,/data/logs/YYYY-MM-DD-HH/logfiles for this given hour, where YYYY-MM-DD-HH changes based on the current log ingest time.,Option A is wrong as the data needs to be processed by day, it would be best to partition the data by date instead of device id.,Options B & D are wrong as DynamoDB is not an ideal solution for storage and archival of logs data and does not provide easy integration with EMR.

正确答案: C

正确答案是C，因为EMR作业需要处理每日数据，最好按天对数据进行分区。请参考AWS文档 – EMR最佳实践。

数据分区是数据处理工作流中的一项重要优化。如果没有进行任何数据分区，数据处理作业需要读取或扫描所有可用的数据集，并应用额外的过滤器以跳过不必要的数据。这种架构对于小数据量可能有效，但对于较大的数据集，扫描整个数据集是一个非常耗时且昂贵的方法。数据分区可以让你创建唯一的数据桶，并消除数据处理作业需要读取整个数据集的需求。

有三个考虑因素决定了如何对数据进行分区：

例如，如果你正在处理一个时间序列数据集，需要每小时处理一次数据，并且你的数据访问模式是基于时间的，那么按日期进行数据分区是最合适的。一个这样的数据处理示例是处理每日日志。如果你有来自各种数据源（如Web服务器、设备等）的日志数据，那么根据一天中的小时来创建数据分区将为你提供基于日期的分区方案。

这种分区方案的结构类似于以下内容：

/data/logs/YYYY-MM-DD-HH/logfiles，表示给定小时的日志文件，其中YYYY-MM-DD-HH根据当前的日志接收时间变化。

选项A是错误的，因为数据需要按天处理，最好按日期而不是设备ID对数据进行分区。

选项B和D是错误的，因为DynamoDB并不是存储和归档日志数据的理想解决方案，并且与EMR的集成不方便。

正确答案: A
Correct answer is A as DynamoDB burst capacity can retain part of unused provisioned capacity, upto 5 minutes, allowing application to burst.,Refer AWS documentation – DynamoDB Best Practices,DynamoDB provides some flexibility in your per-partition throughput provisioning by providing burst capacity, as follows. Whenever you are not fully using a partition’s throughput, DynamoDB reserves a portion of that unused capacity for later bursts of throughput to handle usage spikes.,DynamoDB currently retains up to five minutes (300 seconds) of unused read and write capacity. During an occasional burst of read or write activity, these extra capacity units can be consumed quickly—even faster than the per-second provisioned throughput capacity that you’ve defined for your table.,DynamoDB can also consume burst capacity for background maintenance and other tasks without prior notice.,Option B is wrong as Adaptive Capacity enables your application to continue reading and writing to hot partitions without being throttled, provided that traffic does not exceed your table’s total provisioned capacity or the partition maximum capacity.,Option C is wrong as the partition keys are designed fine, as the application does not consume its total capacity and is not throttled.,Option D is wrong as Write sharding only allows for writes.

正确答案: A

正确答案是A，因为DynamoDB的突发容量可以保留部分未使用的预配置容量，最长可达5分钟，从而允许应用程序进行突发。

参考AWS文档 – DynamoDB最佳实践，DynamoDB通过提供突发容量为您的每个分区的吞吐量预配置提供了一定的灵活性，如下所示。每当您未完全使用一个分区的吞吐量时，DynamoDB会保留该未使用容量的一部分，以供以后进行吞吐量的突发使用，以应对使用峰值。

DynamoDB目前保留最多五分钟（300秒）的未使用读写容量。在偶尔的读写活动突发期间，这些额外的容量单位可以迅速被消耗——甚至比您为表定义的每秒预配置吞吐量还要快。

DynamoDB还可以在没有事先通知的情况下消耗突发容量用于后台维护和其他任务。

选项B是错误的，因为自适应容量使您的应用程序能够继续读取和写入热分区，而不受限制，前提是流量没有超过表的总预配置容量或分区最大容量。

选项C是错误的，因为分区键设计得很好，因为应用程序并未消耗其总容量且不会被限制。

选项D是错误的，因为写入分片仅允许进行写入操作。

正确答案: D
Correct answer is D as the images with metadata can be uploaded to S3. S3 can support both the size and request rate. A Lambda function can be triggered to convert and same the images output back to S3 and metadata to app DB.,Option A is wrong as RDS is not an ideal storage for images as well as handle hundreds of uploads per second.,Options B & C are wrong as Kinesis support max message size of 1MB per record and would not be able to support 10MB of image files.

正确答案: D

正确答案是 D，因为带有元数据的图像可以上传到 S3。S3 可以支持图像的大小和请求速率。可以触发 Lambda 函数来转换并将图像输出保存回 S3，同时将元数据保存到应用数据库。

选项 A 错误，因为 RDS 不是理想的图像存储方式，也无法处理每秒数百次上传。

选项 B 和 C 错误，因为 Kinesis 支持每条记录最大 1MB 的消息大小，无法支持 10MB 的图像文件。

正确答案: A
Correct answer is A as the blood group types are limited, a multi-class classification model can help classification the result into the blood groups,Option B is wrong as regression is for predictive modelling,Option C is wrong as Binary classification can only classify a yes or no.,Option D is wrong as K-Nearest Neighbours is more for grouping unknown data.

正确答案: A

正确答案是 A，因为血型类型是有限的，多类分类模型可以帮助将结果分类到不同的血型中，选项 B 错误，因为回归模型用于预测建模，选项 C 错误，因为二分类只能分类为“是”或“否”，选项 D 错误，因为 K-最近邻更适用于对未知数据进行分组。

正确答案: D
Correct answer is D as the data can be captured using Kinesis Data Stream and Kinesis Data Analytics can be used to query on the streaming data using time or window queries to generate trend analysis.,Refer AWS documentation – Streaming Analytics Pipeline,Many Amazon Web Services (AWS) customers use streaming data to gain real-time insight into customer activity and immediate business trends. Streaming data, which is generated continuously from thousands of data sources, includes a wide variety of data such as log files from your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, financial trading floors, or geospatial services, and telemetry from connected devices. This data can help companies make well-informed decisions and proactively respond to changing business conditions.,Amazon Kinesis, a platform for streaming data on AWS, offers powerful services that make it easier to build data processing applications, load massive volumes of streaming data, and analyze it in real time.,Options A, B & C are wrong as they do not provide analytics on streaming data.

正确答案: D

正确答案是D，因为数据可以通过Kinesis数据流捕获，Kinesis数据分析可以用于对流数据进行时间或窗口查询，以生成趋势分析。参考AWS文档 – 流媒体分析管道。

许多Amazon Web Services (AWS)客户使用流数据来实时洞察客户活动和即时业务趋势。流数据是从成千上万的数据源连续生成的，包含各种各样的数据，如来自您的移动或Web应用程序的日志文件、电子商务购买、游戏中的玩家活动、社交网络信息、金融交易场所或地理空间服务，以及来自连接设备的遥测数据。这些数据可以帮助公司做出明智的决策，并主动应对不断变化的商业环境。

Amazon Kinesis是AWS上的流数据平台，提供强大的服务，使得构建数据处理应用程序、加载大量流数据并实时分析变得更加容易。

选项A、B和C是错误的，因为它们没有提供对流数据的分析。

正确答案: A
Correct answer is A as the data volume is large, it can be processed using EMR to generate structured CSV data and then load the data into Redshift.,Refer AWS documentation – Data Warehousing on AWS,Data in Amazon Redshift must be structured by a defined schema. Amazon Redshift doesn’t support an arbitrary schema structure for each row. If your data is unstructured, you can perform extract, transform, and load (ETL) on Amazon EMR to get the data ready for loading into Amazon Redshift. For JSON data, you can store key value pairs and use the native JSON functions in your queries.,Option B is wrong as unstructured data cannot be loaded into Redshift.,Option C is wrong as Lambda would not be able to handle large amounts of data due to its limitation.,Option D is wrong as market ETL tool is not needed and EMR can be used.

正确答案: A

正确答案是 A，因为数据量很大，可以使用 EMR 处理数据，生成结构化的 CSV 数据，然后将数据加载到 Redshift。

参考 AWS 文档 – AWS 上的数据仓库，Amazon Redshift 中的数据必须由定义的模式进行结构化。Amazon Redshift 不支持每一行具有任意模式结构。如果您的数据是非结构化的，您可以在 Amazon EMR 上执行提取、转换和加载 (ETL)，以使数据准备好加载到 Amazon Redshift。对于 JSON 数据，您可以存储键值对，并在查询中使用原生 JSON 函数。

选项 B 错误，因为非结构化数据无法加载到 Redshift。

选项 C 错误，因为 Lambda 由于其限制，无法处理大量数据。

选项 D 错误，因为不需要市场上的 ETL 工具，可以使用 EMR。

正确答案: A
Key point here is address real time data ingestion.,Correct answer is A,Amazon Kinesis is a platform for streaming data on AWS, making it easy to load and analyze streaming data, and also providing the ability for you to build custom streaming data applications for specialized needs.,Option B is wrong as Data Pipeline is more of an orchestration service and just helps move data between different data stores.,Option C is wrong as
Amazon AppStream is an application streaming service that lets you stream your existing resource-intensive applications from the cloud without code modifications.,Option D is wrong as SQS would not be able to handle large scale real time ingestion.

正确答案: A

关键点在于处理实时数据摄取。

正确答案是A，Amazon Kinesis是AWS上的一个流数据平台，使得加载和分析流数据变得容易，并且还提供了构建定制流数据应用程序的能力，以满足特殊需求。

选项B是错误的，因为Data Pipeline更多的是一个编排服务，仅仅帮助将数据在不同的数据存储之间传输。

选项C是错误的，因为Amazon AppStream是一个应用程序流服务，允许你从云中流式传输现有的资源密集型应用程序，而无需修改代码。

选项D是错误的，因为SQS无法处理大规模的实时摄取。

正确答案: A
Correct answer is A as a single company is facing the issue and it would be a hot key issue cause of the primary key being Company ID. Data Pipeline can be used to migrate the data.,Option B is wrong as Elasticache may be reduce the load depending upon the queries.,Option C is wrong as RDS would not be able to handle the huge amount of data.,Option D is wrong as this is not a cost-effective solution.

正确答案: A

正确答案是A，因为只有一个公司面临这个问题，而且由于主键是公司ID，这将是一个热点问题。数据管道可以用来迁移数据。

选项B是错误的，因为Elasticache可能会根据查询减少负载。

选项C是错误的，因为RDS无法处理大量数据。

选项D是错误的，因为这不是一个具有成本效益的解决方案。

正确答案: C
Correct answer is C as HBase on HDFS provide the ability the store the large amount of data in a non-relational key-value format.,Refer AWS documentation – EMR HBase,HBase is an open source, non-relational, distributed database developed as part of the Apache Software Foundation’s Hadoop project. HBase runs on top of Hadoop Distributed File System (HDFS) to provide non-relational database capabilities for the Hadoop ecosystem.,HBase works seamlessly with Hadoop, sharing its file system and serving as a direct input and output to the MapReduce framework and execution engine. HBase also integrates with Apache Hive, enabling SQL-like queries over HBase tables, joins with Hive-based tables, and support for Java Database Connectivity (JDBC).,Option B is wrong as RDS would not support huge amount of data and is a relational database.,Options A & D is wrong as they do not provide key-value storage format.

正确答案: C

正确答案是C，因为HBase基于HDFS，能够以非关系型键值格式存储大量数据。参考AWS文档 – EMR HBase。

HBase是一个开源的、非关系型的、分布式数据库，是Apache软件基金会Hadoop项目的一部分。HBase运行在Hadoop分布式文件系统（HDFS）之上，为Hadoop生态系统提供非关系型数据库功能。

HBase与Hadoop无缝协作，共享其文件系统，并作为MapReduce框架和执行引擎的直接输入和输出。HBase还与Apache Hive集成，支持对HBase表进行SQL-like查询、与基于Hive的表连接，并支持Java数据库连接（JDBC）。

选项B是错误的，因为RDS不支持大量数据，并且是关系型数据库。

选项A和D是错误的，因为它们不提供键值存储格式。

正确答案: A
Correct answer is A as the provisioned throughput required for DynamoDB would be most cost efficient as compared to the PUT requests for S3.,DynamoDB Calculation vs S3 Calculation (Considering 31 days),Option C is wrong as ElastiCache is not ideal storage, but its more ideal for caching,Options B & D are wrong as PUT operations on S3 for small files would be expensive as compared to DynamoDB inserts.

正确答案: A

正确答案是 A，因为为 DynamoDB 配置的吞吐量相比 S3 的 PUT 请求将更具成本效益。

DynamoDB 计算与 S3 计算（考虑31天），选项 C 错误，因为 ElastiCache 不是理想的存储方案，更适合用于缓存。

选项 B 和 D 错误，因为在 S3 上执行小文件的 PUT 操作比在 DynamoDB 上执行插入操作更昂贵。

正确答案: A
Correct answer is A as Amazon Redshift can use an on-premises HSM for key management over the VPN, which ensures that the encryption keys are locally managed.,Option B is wrong as although it is possible as CloudHSM can cluster to an on-premises HSM. But then key management could be performed on either the on-premises HSM or CloudHSM, and that doesn’t meet the design goal.,Option C is wrong as does not describe a valid feature of KMS and violates the requirement for the corporate HSM to manage the keys requirement, even if it were possible.,Option D is wrong as it is not possible because you cannot put hardware into an AWS Region.

正确答案: A

正确答案是A，因为Amazon Redshift可以通过VPN使用本地的HSM进行密钥管理，这确保了加密密钥由本地管理。

选项B是错误的，尽管CloudHSM可以与本地HSM集群，但密钥管理可以在本地HSM或CloudHSM上进行，这不符合设计目标。

选项C是错误的，因为它没有描述KMS的有效功能，并且违反了公司HSM管理密钥的要求，即使它可能是可行的。

选项D是错误的，因为这是不可能的，因为你不能将硬件放入AWS区域。

正确答案: A
Correct answer is A as Kinesis Data Streams allows the ability for replaying the data as well access to the same data to multiple Kinesis client applications.,Refer AWS documentation – Kinesis Data Streams FAQ,Amazon Kinesis Data Streams enables you to build custom applications that process or analyze streaming data for specialized needs. You can continuously add various types of data such as clickstreams, application logs, and social media to an Amazon Kinesis data stream from hundreds of thousands of sources. Within seconds, the data will be available for your Amazon Kinesis Applications to read and process from the stream.,Amazon Kinesis Data Streams enables real-time processing of streaming big data. It provides ordering of records, as well as the ability to read and/or replay records in the same order to multiple Amazon Kinesis Applications. The Amazon Kinesis Client Library (KCL) delivers all records for a given partition key to the same record processor, making it easier to build multiple applications reading from the same Amazon Kinesis data stream (for example, to perform counting, aggregation, and filtering).,Amazon Simple Queue Service (Amazon SQS) offers a reliable, highly scalable hosted queue for storing messages as they travel between computers. Amazon SQS lets you easily move data between distributed application components and helps you build applications in which messages are processed independently (with message-level ack/fail semantics), such as automated workflows.,Option B is wrong as Kinesis Firehose only allows data transfer to S3, Redshift. It does not provide the replay capability or buffering of messages,Option C is wrong as SNS can provide the message to multiple subscribers, however it cannot replay the data.,Option D is wrong SQS does not provide the ability to replay or access to multiple consumer.

正确答案: A

正确答案是A，因为Kinesis数据流允许重放数据并且可以为多个Kinesis客户端应用程序访问相同的数据。参考AWS文档 – Kinesis数据流常见问题解答。

Amazon Kinesis Data Streams使您能够构建自定义应用程序，处理或分析流数据以满足特殊需求。您可以从成千上万个数据源中将各种类型的数据（例如点击流、应用程序日志和社交媒体数据）持续添加到Amazon Kinesis数据流中。几秒钟内，数据将可供您的Amazon Kinesis应用程序从流中读取和处理。

Amazon Kinesis Data Streams实现了流式大数据的实时处理。它提供记录的顺序处理能力，以及按相同顺序读取和/或重放记录到多个Amazon Kinesis应用程序的能力。Amazon Kinesis客户端库（KCL）将所有记录按给定的分区键交付到同一个记录处理器，使得从同一个Amazon Kinesis数据流读取数据的多个应用程序更容易（例如进行计数、聚合和过滤）。

Amazon Simple Queue Service（Amazon SQS）提供可靠、高度可扩展的托管队列，用于在计算机之间传输消息。Amazon SQS使您能够轻松地在分布式应用程序组件之间移动数据，并帮助您构建消息独立处理的应用程序（具有消息级确认/失败语义），例如自动化工作流。

选项B是错误的，因为Kinesis Firehose仅允许将数据传输到S3、Redshift。它不提供重放能力或消息缓冲。

选项C是错误的，因为SNS可以将消息提供给多个订阅者，但它不能重放数据。

选项D是错误的，因为SQS不提供重放或访问多个消费者的能力。

正确答案: A
Correct answer is A as DynamoDB can store and handle the transactions. DynamoDB also supports strongly consistent reads. DynamoDB is also a managed AWS service.,Amazon DynamoDB is a key-value and document database that delivers single-digit millisecond performance at any scale. It’s a fully managed, multiregion, multimaster database with built-in security, backup and restore, and in-memory caching for internet-scale applications. DynamoDB can handle more than 10 trillion requests per day and support peaks of more than 20 million requests per second.,Option B is wrong as RDS does not meet the storage and request handling rate.,Option C is wrong as NoSQL datastore would need resources and handling. Also, EMR HDFS option is not a cost-effective option.,Option D is wrong ad Redshift is a data warehouse solution and not ideal to handle high frequency data collection.

正确答案: A

正确答案是A，因为DynamoDB可以存储和处理事务。DynamoDB还支持强一致性读取。DynamoDB也是一个托管的AWS服务。

Amazon DynamoDB是一个键值和文档数据库，能够在任何规模下提供单数字毫秒级性能。它是一个完全托管的、多区域、多主数据库，具有内置的安全性、备份和恢复功能，并为互联网规模的应用提供内存缓存。DynamoDB每天可以处理超过10万亿个请求，并支持超过每秒2000万个请求的高峰。

选项B是错误的，因为RDS无法满足存储和请求处理速率的需求。

选项C是错误的，因为NoSQL数据存储需要资源和处理。同时，EMR HDFS选项不是一种成本效益高的选择。

选项D是错误的，因为Redshift是一个数据仓库解决方案，并不适合处理高频数据收集。

正确答案: A
Correct answer is A as the SecurityAudit managed policy can provide the Auditors with the read only access to AWS Services.,Option B is wrong as providing SSH keys is not a good practice.,Option C is wrong as it does not mention the Cluster was setup using CloudFormation. Also, CloudFormation templates may not give the actual picture of whats deployed.,Option D is wrong as Direct Connect does not provide access to tools and it is still control using IAM.

正确答案: A

正确答案是A，因为SecurityAudit托管策略可以为审计员提供只读访问AWS服务的权限。

选项B是错误的，因为提供SSH密钥不是一个好的做法。

选项C是错误的，因为它没有提到集群是通过CloudFormation设置的。此外，CloudFormation模板可能无法给出实际部署的情况。

选项D是错误的，因为Direct Connect不提供对工具的访问，并且仍然是通过IAM进行控制。

正确答案: C
Correct answer is C as by default, Kinesis stores the records for 24 hours only.,Refer AWS documentation – Kinesis FAQs,By default, Records of a stream are accessible for up to 24 hours from the time they are added to the stream. You can raise this limit to up to 7 days by enabling extended data retention.

正确答案: C

正确答案是C，因为默认情况下，Kinesis 只会存储记录 24 小时。

请参考 AWS 文档 – Kinesis 常见问题解答。

默认情况下，流的记录在添加到流中后的 24 小时内可访问。您可以通过启用延长数据保留来将此限制提高到最多 7 天。

正确答案: A
Key point here is to save most money while being able to process the huge data.,Correct answer is A as it follows best practice of using On demand for master and core and spot for task nodes also help reduce cost using spot instances.,Refer AWS documentation – EMR Instances,Option B is wrong as RI will make it expensive as there is no consistent requirement,Option C is wrong as RI will make it expensive as there is no consistent requirement.,Option D is wrong as input should be in S3 standard, as re-ingesting the input data might end up being more costly then holding the data for limited time in standard S3

正确答案: A

这里的关键是节省大部分费用，同时能够处理庞大的数据。

正确答案是A，因为它遵循了最佳实践，即为主节点和核心节点使用按需实例，为任务节点使用竞价实例，从而帮助降低成本。

请参考AWS文档 – EMR实例。

选项B是错误的，因为预留实例（RI）会使其变得昂贵，因为没有持续的需求。

选项C是错误的，因为预留实例（RI）会使其变得昂贵，因为没有持续的需求。

选项D是错误的，因为输入数据应该存储在S3标准存储中，因为重新加载输入数据可能比将数据在标准S3中保存有限时间更为昂贵。

正确答案: A
Correct answer is A as Identity Federation allows organizations to associate temporary credentials to users authenticated through an external identity provider such as Microsoft Active Directory (AD). These temporary credentials are linked to AWS IAM roles that grant access to the S3 bucket.,Refer AWS documentation – S3 Cross Account Access (Role to S3 Bucket with Prefix access can be configured similarly),Option B is wrong as it does not work because bucket policies are linked to IAM principles and cannot recognize AD attributes.,Option C is wrong as it does not work because AD Synchronization will not sync directly with AWS IAM, and custom synchronization would not result in Amazon S3 being able to see group information.,Option D is wrong as it isn’t possible because there is no feature to integrate Amazon S3 directly with external identity providers.,Correct answer is B as
NOLOAD checks the integrity of all of the data without loading it into the database. The NOLOAD option displays any errors that would occur if you had attempted to load the data. All other options will require subsequent processing on the cluster which will consume resources.,Refer AWS documentation – Data Load Copy Parameters,If you want to validate your data without actually loading the table, use the NOLOAD option with the COPY command.

正确答案: A

正确答案是A，因为身份联合允许组织将临时凭证与通过外部身份提供者（如Microsoft Active Directory (AD)）验证的用户相关联。这些临时凭证与AWS IAM角色关联，这些角色授予访问S3存储桶的权限。参考AWS文档 – S3跨账户访问（可以类似配置角色到S3存储桶与前缀访问）

选项B是错误的，因为它不起作用，因为存储桶策略与IAM原则关联，无法识别AD属性。

选项C是错误的，因为它不起作用，因为AD同步不会直接与AWS IAM同步，并且自定义同步不会导致Amazon S3能够看到组信息。

选项D是错误的，因为这是不可能的，因为没有将Amazon S3直接与外部身份提供者集成的功能。

正确答案是B，因为NOLOAD选项检查所有数据的完整性，而无需将其加载到数据库中。NOLOAD选项会显示如果尝试加载数据时可能出现的错误。所有其他选项将需要在集群上进行后续处理，这将消耗资源。

参考AWS文档 – 数据加载复制参数

如果您想验证数据而不实际加载表格，请在COPY命令中使用NOLOAD选项。

正确答案: A
Correct answer is A as EMRFS consistent view using a DynamoDB table can be implemented using a separate common DynamoDB table.,Refer AWS documentation – EMRFS Metadata,EMRFS consistent view tracks consistency using a DynamoDB table to track objects in Amazon S3 that have been synced with or created by EMRFS. The metadata is used to track all operations (read, write, update, and copy), and no actual content is stored in it. This metadata is used to validate whether the objects or metadata received from Amazon S3 matches what is expected. This confirmation gives EMRFS the ability to check list consistency and read-after-write consistency for new objects EMRFS writes to Amazon S3 or objects synced with EMRFS. Multiple clusters can share the same metadata.

正确答案: A

正确答案是 A，因为使用 DynamoDB 表可以实现 EMRFS 一致性视图，并且可以通过一个单独的公共 DynamoDB 表来实现。

参见 AWS 文档 – EMRFS 元数据

EMRFS 一致性视图通过使用 DynamoDB 表来跟踪 Amazon S3 中的对象，这些对象已经与 EMRFS 同步或由 EMRFS 创建。元数据用于跟踪所有操作（读取、写入、更新和复制），并且不会在其中存储实际内容。该元数据用于验证从 Amazon S3 接收的对象或元数据是否与预期的内容匹配。此确认使 EMRFS 能够检查列表一致性，并且可以为 EMRFS 写入到 Amazon S3 或与 EMRFS 同步的新对象提供写后读一致性。

多个集群可以共享相同的元数据。

正确答案: D
Correct answer is D as the key point here is batch application and message being stored durably and delivery guarantee. Kinesis can store the data durably and allow access to multiple consumers without any dependencies.,Refer AWS documentation – Kinesis Data Streams,Q: How does Amazon Kinesis Data Streams differ from Amazon SQS?,Amazon Kinesis Data Streams enables real-time processing of streaming big data. It provides ordering of records, as well as the ability to read and/or replay records in the same order to multiple Amazon Kinesis Applications. The Amazon Kinesis Client Library (KCL) delivers all records for a given partition key to the same record processor, making it easier to build multiple applications reading from the same Amazon Kinesis data stream (for example, to perform counting, aggregation, and filtering).,Amazon Simple Queue Service (Amazon SQS) offers a reliable, highly scalable hosted queue for storing messages as they travel between computers. Amazon SQS lets you easily move data between distributed application components and helps you build applications in which messages are processed independently (with message-level ack/fail semantics), such as automated workflows.,Q: When should I use Amazon Kinesis Data Streams, and when should I use Amazon SQS?,We recommend Amazon Kinesis Data Streams for use cases with requirements that are similar to the following:,We recommend Amazon SQS for use cases with requirements that are similar to the following:,Option A is wrong as SQS chaining would create dependency among the consumers. If one consumer fails, the message would not be available for the other consumer impacting the availability.,Option B is wrong as although possible is error prone and needs to maintain the position read by the application.,Option C is wrong as with DynamoDB delivery guarantee needs to handled by application as well is not a cost effective solution.

正确答案: D

Correct answer is D as the key point here is batch application and message being stored durably and delivery guarantee. Kinesis can store the data durably and allow access to multiple consumers without any dependencies.

Refer AWS documentation – Kinesis Data Streams

Q: How does Amazon Kinesis Data Streams differ from Amazon SQS?

Amazon Kinesis Data Streams enables real-time processing of streaming big data. It provides ordering of records, as well as the ability to read and/or replay records in the same order to multiple Amazon Kinesis Applications. The Amazon Kinesis Client Library (KCL) delivers all records for a given partition key to the same record processor, making it easier to build multiple applications reading from the same Amazon Kinesis data stream (for example, to perform counting, aggregation, and filtering).

Amazon Simple Queue Service (Amazon SQS) offers a reliable, highly scalable hosted queue for storing messages as they travel between computers. Amazon SQS lets you easily move data between distributed application components and helps you build applications in which messages are processed independently (with message-level ack/fail semantics), such as automated workflows.

Q: When should I use Amazon Kinesis Data Streams, and when should I use Amazon SQS?

We recommend Amazon Kinesis Data Streams for use cases with requirements that are similar to the following:

We recommend Amazon SQS for use cases with requirements that are similar to the following:

Option A is wrong as SQS chaining would create dependency among the consumers. If one consumer fails, the message would not be available for the other consumer impacting the availability.

Option B is wrong as although possible is error prone and needs to maintain the position read by the application.

Option C is wrong as with DynamoDB delivery guarantee needs to handled by application as well is not a cost effective solution.

正确答案: A
Key here the cost efficient solution with company needing video transcoding expertise and needing to hire a consultant with global distribution.,Correct answer is A as Elastic Transcoder provides and out of box option to transcode videos into any format without any expertise. S3 to host videos and CloudFront to serve HLS transcoded videos for global distribution while being cost efficient,Option B & D are wrong as a video transcoding pipeline with instances would increase the cost needing expertise as well as infrastructure,Option C & D are wrong as EBS volumes to host data with snapshots would increase the cost.

正确答案: A

关键在于成本高效的解决方案，公司需要视频转码专业知识，并需要雇佣具有全球分发能力的顾问。正确答案是A，因为Elastic Transcoder提供了一种开箱即用的选项，可以将视频转码为任何格式，无需任何专业知识。使用S3托管视频，使用CloudFront提供HLS转码视频以实现全球分发，同时保持成本高效。

选项B和D是错误的，因为视频转码管道需要实例，这会增加成本并需要专业知识以及基础设施。

选项C和D是错误的，因为使用EBS卷托管数据并创建快照会增加成本。

正确答案: C
Correct answer is C as Supervised learning using Regression can help build a model to predict rain based on the historical data.,Refer documentation – Machine Learning

正确答案: C

正确答案是C，因为使用回归的监督学习可以帮助建立一个基于历史数据预测降雨的模型。

参考文档 – 机器学习

正确答案: C
Correct answer is C as Jupyter Notebook is an open-source web application that you can use to create and share documents that contain live code, equations, visualizations, and narrative text,Option A is wrong as Hive is an open-source, data warehouse, and analytic package that runs on top of a Hadoop cluster.,Option B is wrong as Apache Hue is an open-source, web-based, graphical user interface for use with Amazon EMR and Apache Hadoop. It does not provide live code and sharing of documents.,Option D is wrong as Presto is a fast SQL query engine designed for interactive analytic queries over large datasets from multiple sources

正确答案: C

正确答案是 C，因为 Jupyter Notebook 是一个开源的 web 应用程序，您可以使用它创建和共享包含实时代码、方程式、可视化和叙述文本的文档。

选项 A 错误，因为 Hive 是一个开源的数据仓库和分析包，运行在 Hadoop 集群之上。

选项 B 错误，因为 Apache Hue 是一个开源的基于 web 的图形用户界面，用于与 Amazon EMR 和 Apache Hadoop 配合使用。它不提供实时代码和文档共享功能。

选项 D 错误，因为 Presto 是一个快速的 SQL 查询引擎，专为对来自多个来源的大型数据集执行交互式分析查询而设计。

正确答案: A
Correct answer is A as the simplest would be to use Firehose to stream data to collect the logs and load the data to Redshift for analysis.,Refer AWS documentation – Kinesis Data Firehose,Amazon Kinesis Data Firehose is the easiest way to reliably load streaming data into data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can also batch, compress, transform, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security.,Option B is wrong the logs file cannot be written to S3 directly and would need an agent like CloudWatch for Kinesis. Also you would need to develop the Lambda function to track events and load data to Elasticsearch,Option C is wrong as local disk would not provide the durability and might lead to data loss if the instance goes down.,Option D is wrong as the Lambda functions needs to be developed. Also, loading the data to HDFS using lambda might not seamless and would need AWS Data Pipeline.

正确答案: A

正确答案是A，因为最简单的方法是使用Firehose流式传输数据来收集日志，并将数据加载到Redshift进行分析。

参考AWS文档 – Kinesis Data Firehose。

Amazon Kinesis Data Firehose是将流数据可靠地加载到数据存储和分析工具的最简单方法。它可以捕获、转换并将流数据加载到Amazon S3、Amazon Redshift、Amazon Elasticsearch Service和Splunk，支持使用您今天已经在使用的现有业务智能工具和仪表板进行接近实时的分析。它是一个完全托管的服务，能够自动扩展以匹配数据吞吐量，并且无需持续管理。它还可以在加载数据之前对其进行批处理、压缩、转换和加密，从而最大限度地减少目标存储的使用量并提高安全性。

选项B是错误的，因为日志文件不能直接写入S3，需要像CloudWatch这样的代理来进行Kinesis。同时，您需要开发Lambda函数来跟踪事件并将数据加载到Elasticsearch。

选项C是错误的，因为本地磁盘无法提供耐用性，如果实例崩溃，可能会导致数据丢失。

选项D是错误的，因为需要开发Lambda函数。而且，通过Lambda加载数据到HDFS可能不够无缝，并且需要AWS Data Pipeline。

正确答案: A
Correct answer is A as IoT rules can help evaluate and send notifications when the peak thresholds are exceeded.
The AWS IoT rules engine listens for incoming MQTT messages that match a rule. When a matching message is received, the rule takes some action with the data in the MQTT message (for example, writing data to an Amazon S3 bucket, invoking a Lambda function, or sending a message to an Amazon SNS topic).,Refer AWS documentation – IoT Rules,Rules give your devices the ability to interact with AWS services. Rules are analyzed and actions are performed based on the MQTT topic stream. You can use rules to support tasks like these:,Options B, C & D are wrong as they are not cost-effective and need additional development or involve other services.

正确答案: A

正确答案是A，因为IoT规则可以帮助评估并在超过峰值阈值时发送通知。

AWS IoT规则引擎监听与规则匹配的传入MQTT消息。当收到匹配的消息时，规则会对MQTT消息中的数据执行某些操作（例如，将数据写入Amazon S3存储桶、调用Lambda函数或将消息发送到Amazon SNS主题）。

参考AWS文档 – IoT规则

规则使您的设备能够与AWS服务进行交互。规则会根据MQTT主题流进行分析，并执行相应的操作。您可以使用规则来支持以下任务：

选项B、C和D是错误的，因为它们

正确答案: A
Correct answer is A as Kinesis Data Streams with Kinesis Data Analytics can provide real time analytics only data while using managed services,Refer AWS documentation – Kinesis Data Analytics,Amazon Kinesis Data Analytics is the easiest way to analyze streaming data, gain actionable insights, and respond to your business and customer needs in real time. Amazon Kinesis Data Analytics reduces the complexity of building, managing, and integrating streaming applications with other AWS services. SQL users can easily query streaming data or build entire streaming applications using templates and an interactive SQL editor. Java developers can quickly build sophisticated streaming applications using open source Java libraries and AWS integrations to transform and analyze data in real-time.,Amazon Kinesis Data Analytics takes care of everything required to run your real-time applications continuously and scales automatically to match the volume and throughput of your incoming data.,Option B is wrong as Kafka needs to be managed and does not provide analytical capability,Option C is wrong as Athena does not work on the streaming analytics. It is more for batch analytics,Option D is wrong as Spark Streaming and MLlib would need EMR cluster management and development.

正确答案: A

正确答案是A，因为Kinesis数据流与Kinesis数据分析可以提供实时分析数据，同时使用托管服务。参考AWS文档 – Kinesis数据分析，Amazon Kinesis数据分析是分析流数据、获取可操作的洞察并实时响应业务和客户需求的最简单方式。Amazon Kinesis数据分析减少了构建、管理和与其他AWS服务集成流应用程序的复杂性。SQL用户可以轻松查询流数据或使用模板和交互式SQL编辑器构建整个流应用程序。Java开发人员可以快速使用开源Java库和AWS集成构建复杂的流应用程序，以实时转换和分析数据。

Amazon Kinesis数据分析负责处理运行实时应用程序所需的一切，并自动扩展以匹配传入数据的量和吞吐量。

选项B是错误的，因为Kafka需要管理，并且不提供分析能力。

选项C是错误的，因为Athena不适用于流数据分析。它更适用于批量分析。

选项D是错误的，因为Spark Streaming和MLlib需要EMR集群管理和开发。

正确答案: D
Correct answer is D as the data is already hosted in S3, EMR with EMRFS can be used to perform analysis.,Option A is wrong EBS backed volumes cannot handled the data capacity,Option B is wrong as copying the data in Redshift would duplicate it and current size limitation of Redshift is 2PB.,Option C is wrong as the answer is incomplete as it does not provide any analysis option.

正确答案: D

正确答案是 D，因为数据已经托管在 S3 中，EMR 可以与 EMRFS 一起使用来执行分析。

选项 A 错误，因为 EBS 支持的卷无法处理数据容量。

选项 B 错误，因为将数据复制到 Redshift 会导致数据重复，并且 Redshift 当前的大小限制是 2PB。

选项 C 错误，因为答案不完整，没有提供任何分析选项。

正确答案: B
Correct answer is B as gzip is not ideal compression for files larger than 1GB and compression technique should be checked with supports splitting like bzip2 or one with higher compression handling like Snappy.,Refer AWS documentation – EMR Best Practices,Depending on how large your aggregated data files are, the compression algorithm becomes an important choice. For instance, if you are aggregating your data (using the ingest tool of your choice) and the aggregated data files are between 500 MB to 1 GB, GZIP compression is an acceptable data compression type. However, if your data aggregation creates files larger than 1 GB, its best to pick a compression algorithm that supports splitting.,What Compression Algorithm Should I Use?,Naturally, not all compression algorithms are alike. Consider these potential advantages and disadvantages:, As the table below suggests, some compression algorithms are faster. You need to understand your workload in order to decide if faster compressions are any use for you. For example, if your job is CPU bounded, faster compression algorithms may not give you enough performance improvement. If you decide compression speed is important, Snappy compression seems to perform faster., Some compressions algorithms are slower but offer better space savings, which may be important to you. However, if storage cost is not an important factor, you may want a faster algorithm instead., Importantly, some algorithms allow file output to be split. As discussed earlier, the ability to split your data file affects how you store your data files. If the compression algorithm does not support splitting, you may have to maintain smaller file sizes. However, if your compressed files can be chunked, you may want to store large files for Amazon EMR processing.

正确答案: B

正确答案是B，因为gzip不适合用于大于1GB的文件压缩，应该选择支持分割的压缩技术，例如bzip2，或者像Snappy那样支持更高压缩处理的技术。参考AWS文档 – EMR最佳实践。

根据你聚合的数据文件的大小，压缩算法是一个重要的选择。例如，如果你正在聚合数据（使用你选择的摄取工具），而聚合后的数据文件大小在500MB到1GB之间，GZIP压缩是一个可接受的数据压缩类型。然而，如果你的数据聚合创建了大于1GB的文件，最好选择一个支持分割的压缩算法。

应该使用什么压缩算法？

自然，并非所有压缩算法都相同。考虑以下潜在的优缺点：

• 如表格所示，一些压缩算法更快。你需要了解你的工作负载，以决定是否更快的压缩对你有用。例如，如果你的任务是CPU绑定的，快速的压缩算法可能不会带来足够的性能提升。如果你决定压缩速度很重要，Snappy压缩似乎表现得更快。
• 一些压缩算法较慢，但能提供更好的空间节省，这对你可能很重要。然而，如果存储成本不是一个重要因素，你可能更希望选择一个更快的算法。
• 重要的是，一些算法允许文件输出分割。如前所述，数据文件分割的能力会影响你如何存储数据文件。如果压缩算法不支持分割，你可能需要维持较小的文件大小。然而，如果你的压缩文件可以分块，你可能希望将大文件存储用于Amazon EMR处理。

正确答案: A
Correct answer is A as machine learning are usually compute intensive and adhoc queries are suited for memory optimized.,Refer AWS documentation – EMR Best Practices,For memory-intensive applications, prefer R type instances over the other instance types. For compute-intensive applications, prefer C type instances. For applications balanced between memory and compute, prefer M type general-purpose instances.,Compute Optimized – High performance web servers, scientific modelling, batch processing, distributed analytics, high-performance computing (HPC), machine/deep learning inference, ad serving, highly scalable multiplayer gaming, and video encoding.,Memory Optimized – Instances are well suited for memory intensive applications such as high performance databases, distributed web scale in-memory caches, mid-size in-memory databases, real time big data analytics, and other enterprise applications.,Option B is wrong as G2 is GPU powered and GPU-powered G2 instance family is home to molecular modeling, rendering, machine learning, game streaming, and transcoding jobs that require massive amounts of parallel processing power.,Option C is wrong as T are ideally suited for general purpose applications.,Option D is wrong as I & D are storage optimized instances.

正确答案: A

正确答案是A，因为机器学习通常需要大量计算资源，而临时查询适合于内存优化的环境。请参考AWS文档 – EMR最佳实践。

对于内存密集型应用程序，优先选择R类型实例而非其他类型的实例。对于计算密集型应用程序，优先选择C类型实例。对于在内存和计算之间平衡的应用程序，优先选择M类型通用实例。

计算优化型 – 高性能网页服务器、科学建模、批处理、分布式分析、高性能计算（HPC）、机器/深度学习推理、广告服务、高度可扩展的多人游戏以及视频编码。

内存优化型 – 实例非常适合内存密集型应用程序，如高性能数据库、分布式网页规模内存缓存、中型内存数据库、实时大数据分析及其他企业应用。

选项B错误，因为G2是GPU驱动的，GPU驱动的G2实例系列适用于分子建模、渲染、机器学习、游戏流媒体和转码任务，这些任务需要大量并行处理能力。

选项C错误，因为T实例类型理想适用于通用应用程序。

选项D错误，因为I和D是存储优化型实例。

正确答案: B, E
Correct answer is B & E as the key point here is to reduce job completion time.,Option B as the split size of the match in memory block size of task and HDFS files will help to complete the job faster.,Option E as adjusting and tuning the number of simultaneous mapper task would help reduce time,Refer to EMR Best Practices,Option A is wrong as Spot instances would help reduce cost but might increase the job completion time,Option C is wrong as it would not help as the data is already there in the data nodes.,Option D is wrong as the instances would be in VPC already and would not improve job times,Option F is wrong as termination protection would not help as the instances are not being terminated adhoc

正确答案: B, E

正确答案是B和E，因为关键点在于减少作业完成时间。

选项B，因为内存块大小的匹配和HDFS文件的拆分大小将有助于更快地完成作业。

选项E，因为调整和调整同时进行的mapper任务数量有助于减少时间。

参考EMR最佳实践。

选项A是错误的，因为Spot实例有助于降低成本，但可能会增加作业完成时间。

选项C是错误的，因为它没有帮助，因为数据已经存在于数据节点中。

选项D是错误的，因为实例已经在VPC中，不会改善作业时间。

选项F是错误的，因为终止保护不会起作用，因为这些实例不是临时终止的。

正确答案: A
Correct answer is A as the most cost effective solution is to use a transient cluster to create the stats and use S3 to host the same.,Option B is wrong as using MicroStrategy is an overhead and its a marketplace product, which would not be a cost effective solution.,Options C & D are wrong as using a long running or continuous cluster is not cost effective

正确答案: A

正确答案是 A，因为最具成本效益的解决方案是使用临时集群来创建统计数据，并使用 S3 托管相同的内容。

选项 B 错误，因为使用 MicroStrategy 是一种负担，并且它是一个市场产品，这不是一种成本效益高的解决方案。

选项 C 和 D 错误，因为使用长期运行或持续集群并不具有成本效益。

正确答案: B
Key point is perform ad-hoc analytics with data at high velocity,Correct answer is B as Kinesis Firehose provides a managed service for aggregating streaming data and inserting it into RedShift. RedShift also supports ad-hoc queries over well-structured data using a SQL-compliant wire protocol, so the business team should be able to adopt this system easily.,Option A is wrong as RDS would not be suitable for ad-hoc analytics,Option C & D are wrong as EMR does not itself handle data at high velocity. Need Kafka or similar frameworks

正确答案: B

关键点是以高速度对数据进行临时分析，正确答案是 B，因为 Kinesis Firehose 提供了一个托管服务，用于聚合流数据并将其插入 RedShift。RedShift 还支持对结构良好的数据进行临时查询，使用符合 SQL 协议的网络协议，因此业务团队应该能够轻松采用此系统。

选项 A 错误，因为 RDS 不适合临时分析。

选项 C 和 D 错误，因为 EMR 本身无法处理高速数据。需要 Kafka 或类似的框架。

正确答案: A
Correct answer is A as Regression model can help predict or forecast data based on the earlier dataset.,Regression predictive modeling is the task of approximating a mapping function (f) from input variables (X) to a continuous output variable (y).,Option B is wrong as Random forest is not needed and Spark MLib needs to have EMR to run, while Amazon Machine Learning is quick.,Options C & D are wrong as they are only or classification and do not forecast values.

正确答案: A

正确答案是A，因为回归模型可以基于早期数据集帮助预测或预报数据。回归预测建模是近似映射函数（f）的任务，输入变量（X）映射到连续输出变量（y）。

选项B是错误的，因为随机森林不需要，而Spark MLib需要有EMR来运行，而Amazon机器学习则很快速。

选项C和D是错误的，因为它们仅用于分类，不能预测值。