An ecommerce company ingests a large set of clickstream data in JSON format and stores the data in Amazon S3. Business analysts from multiple product divisions need to use Amazon Athena to analyze the dat a. The company's analytics team must design a solution to monitor the daily data usage for Athena by each product division. The solution also must produce a warning when a divisions exceeds its quota Which solution will meet these requirements with the LEAST operational overhead?

• A.Create an AWS account for each division Provide cross-account access to an AWS Glue Data Catalog to all the accounts. Set an Amazon CloudWatch alarm to monitor Athena usage. Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
• B.Use a CREATE TABLE AS SELECT (CTAS) statement to create separate tables for each product division Use AWS Budgets to track Athena usage Configure a threshold for the budget Use Amazon Simple Notification Service (Amazon SNS) to send notifications when thresholds are breached.
• C.Create an AWS account for each division Configure an AWS Glue Data Catalog in each account Set an Amazon CloudWatch alarm to monitor Athena usage Use Amazon Simple Notification Service (Amazon SNS) to send notifications.
• D.Create an Athena workgroup for each division Configure a data usage control for each workgroup and a time period of 1 day Configure an action to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic

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A company is planning to create a data lake in Amazon S3. The company wants to create tiered storage based on access patterns and cost objectives. The solution must include support for JDBC connections from legacy clients, metadata management that allows federation for access control, and batch-based ETL using PySpark and Scala Operational management should be limited.
Which combination of components can meet these requirements? (Choose three.)

• A.Amazon EMR with Apache Hive, using an Amazon RDS with MySQL-compatible backed metastore
• B.Amazon EMR with Apache Spark for ETL
• C.Amazon EMR with Apache Hive for JDBC clients
• D.Amazon Athena for querying data in Amazon S3 using JDBC drivers
• E.AWS Glue Data Catalog for metadata management
• F.AWS Glue for Scala-based ETL

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A team of data scientists plans to analyze market trend data for their company's new investment strategy. The trend data comes from five different data sources in large volumes. The team wants to utilize Amazon Kinesis to support their use case. The team uses SQL-like queries to analyze trends and wants to send notifications based on certain significant patterns in the trends. Additionally, the data scientists want to save the data to Amazon S3 for archival and historical re-processing, and use AWS managed services wherever possible. The team wants to implement the lowest-cost solution.
Which solution meets these requirements?

• A.Publish data to one Kinesis data stream. Deploy Kinesis Data Analytic to the stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
  Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.
• B.Publish data to two Kinesis data streams. Deploy a custom application using the Kinesis Client Library (KCL) to the first stream for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• C.Publish data to two Kinesis data streams. Deploy Kinesis Data Analytics to the first stream for analyzing trends, and configure an AWS Lambda function as an output to send notifications using Amazon SNS.
  Configure Kinesis Data Firehose on the second Kinesis data stream to persist data to an S3 bucket.
• D.Publish data to one Kinesis data stream. Deploy a custom application using the Kinesis Client Library (KCL) for analyzing trends, and send notifications using Amazon SNS. Configure Kinesis Data Firehose on the Kinesis data stream to persist data to an S3 bucket.

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A banking company is currently using an Amazon Redshift cluster with dense storage (DS) nodes to store sensitive data. An audit found that the cluster is unencrypted. Compliance requirements state that a database with sensitive data must be encrypted through a hardware security module (HSM) with automated key rotation.
Which combination of steps is required to achieve compliance? (Choose two.)

• A.Set up a trusted connection with HSM using a client and server certificate with automatic key rotation.
• B.Enable HSM with key rotation through the AWS CLI.
• C.Enable Elliptic Curve Diffie-Hellman Ephemeral (ECDHE) encryption in the HSM.
• D.Modify the cluster with an HSM encryption option and automatic key rotation.
• E.Create a new HSM-encrypted Amazon Redshift cluster and migrate the data to the new cluster.

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A media company wants to perform machine learning and analytics on the data residing in its Amazon S3 data lake. There are two data transformation requirements that will enable the consumers within the company to create reports:
* Daily transformations of 300 GB of data with different file formats landing in Amazon S3 at a scheduled time.
* One-time transformations of terabytes of archived data residing in the S3 data lake.
Which combination of solutions cost-effectively meets the company's requirements for transforming the data?
(Choose three.)

• A.For daily incoming data, use AWS Glue workflows with AWS Glue jobs to perform transformations.
• B.For archived data, use Amazon SageMaker to perform data transformations.
• C.For daily incoming data, use Amazon Redshift to perform transformations.
• D.For archived data, use Amazon EMR to perform data transformations.
• E.For daily incoming data, use AWS Glue crawlers to scan and identify the schema.
• F.For daily incoming data, use Amazon Athena to scan and identify the schema.

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