A company stores revenue data in Amazon Redshift. A data analyst needs to create a dashboard so that the company's sales team can visualize historical revenue and accurately forecast revenue for the upcoming months.
Which solution will MOST cost-effectively meet these requirements?

A. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a custom field in QuickSight that applies a linear regression function to the data. Publish the analysis as a dashboard.
B. Create a JavaScript dashboard by using D3.js charts and the data in Amazon Redshift. Export the data to Amazon SageMaker. Run a Python script to run a regression model to forecast revenue. Import the data back into Amazon Redshift. Add the new forecast information to the dashboard.
C. Create an Amazon QuickSight analysis by using the data in Amazon Redshift. Add a forecasting widget. Publish the analysis as a dashboard.
D. Create an Amazon SageMaker model for forecasting. Integrate the model with an Amazon QuickSight dataset. Create a widget for the dataset. Publish the analysis as a dashboard.

A company plans to provision a log delivery stream within a VPC. The company configured the VPC flow logs to publish to Amazon CloudWatch Logs. The company needs to send the flow logs to Splunk at a near-real-time rate for further analysis.
Which solution will meet these requirements with the LEAST operational overhead?

A. Configure an Amazon Kinesis data stream with Splunk as a destination. Create a CloudWatch Logs subscription filter to send log events to the data stream.
B. Create an Amazon Kinesis Data Firehose delivery stream with Splunk as a destination. Create a CloudWatch Logs subscription filter to send log events to the delivery stream.
C. Create an Amazon Kinesis Data Firehose delivery stream with Splunk as a destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the delivery stream.
D. Configure an Amazon Kinesis data stream with Splunk as a destination. Create an AWS Lambda function to send the flow logs from CloudWatch Logs to the data stream.

A company hosts an Apache Flink application on premises. The application processes data from several Apache Kafka clusters. The data originates from a variety of sources, such as web applications, mobile apps, and operational databases. The company has migrated some of these sources to AWS and now wants to migrate the Flink application. The company must ensure that data that resides in databases within the VPC does not traverse the internet. The application must be able to process all the data that comes from the company's AWS solution, on-premises resources, and the public internet.
Which solution will meet these requirements with the LEAST operational overhead?

A. Implement Flink on Amazon EC2 within the company's VPC. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the VPC to collect data that comes from applications and databases within the VPC. Use Amazon Kinesis Data Streams to collect data that comes from the public internet. Configure Flink to have sources from Kinesis Data Streams, Amazon MSK, and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.
B. Implement Flink on Amazon EC2 within the company's VPC. Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet. Configure Flink to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.
C. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Use Amazon Kinesis Data Streams to collect data that comes from applications and databases within the VPC and the public internet. Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.
D. Create an Amazon Kinesis Data Analytics application by uploading the compiled Flink .jar file. Create Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters in the company's VPC to collect data that comes from applications and databases within the VPC. Use Amazon Kinesis Data Streams to collect data that comes from the public internet. Configure the Kinesis Data Analytics application to have sources from Kinesis Data Streams, Amazon MSK, and any on-premises Kafka clusters by using AWS Client VPN or AWS Direct Connect.

A gaming company is collecting clickstream data into multiple Amazon Kinesis data streams. The company uses Amazon Kinesis Data Firehose delivery streams to store the data in JSON format in Amazon S3. Data scientists use Amazon Athena to query the most recent data and derive business insights. The company wants to reduce its Athena costs without having to recreate the data pipeline. The company prefers a solution that will require less management effort.
Which set of actions can the data scientists take immediately to reduce costs?

A. Change the Kinesis Data Firehose output format to Apache Parquet. Provide a custom S3 object YYYYMMDD prefix expression and specify a large buffer size. For the existing data, run an AWS Glue ETL job to combine and convert small JSON files to large Parquet files and add the YYYYMMDD prefix. Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
B. Create an Apache Spark job that combines and converts JSON files to Apache Parquet files. Launch an Amazon EMR ephemeral cluster daily to run the Spark job to create new Parquet files in a different S3 location. Use ALTER TABLE SET LOCATION to reflect the new S3 location on the existing Athena table.
C. Create a Kinesis data stream as a delivery target for Kinesis Data Firehose. Run Apache Flink on Amazon Kinesis Data Analytics on the stream to read the streaming data, aggregate it. and save it to Amazon S3 in Apache Parquet format with a custom S3 object YYYYMMDD prefix. Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.
D. Integrate an AWS Lambda function with Kinesis Data Firehose to convert source records to Apache Parquet and write them to Amazon S3. In parallel, run an AWS Glue ETL job to combine and convert existing JSON files to large Parquet files. Create a custom S3 object YYYYMMDD prefix. Use ALTER TABLE ADD PARTITION to reflect the partition on the existing Athena table.

A global company has different sub-organizations, and each sub-organization sells its products and services in various countries. The company's senior leadership wants to quickly identify which sub-organization is the strongest performer in each country. All sales data is stored in Amazon S3 in Parquet format.
Which approach can provide the visuals that senior leadership requested with the least amount of effort?

A. Use Amazon QuickSight with Amazon Athena as the data source. Use heat maps as the visual type.
B. Use Amazon QuickSight with Amazon S3 as the data source. Use heat maps as the visual type.
C. Use Amazon QuickSight with Amazon Athena as the data source. Use pivot tables as the visual type.
D. Use Amazon QuickSight with Amazon S3 as the data source. Use pivot tables as the visual type.

An online retail company uses Amazon Redshift to store historical sales transactions. The company is required to encrypt data at rest in the clusters to comply with the Payment Card Industry Data Security Standard (PCI DSS). A corporate governance policy mandates management of encryption keys using an on-premises hardware security module (HSM).
Which solution meets these requirements?

A. Create and manage encryption keys using AWS CloudHSM Classic. Launch an Amazon Redshift cluster in a VPC with the option to use CloudHSM Classic for key management.
B. Create a VPC and establish a VPN connection between the VPC and the on-premises network. Create an HSM connection and client certificate for the on-premises HSM. Launch a cluster in the VPC with the option to use the on-premises HSM to store keys.
C. Create an HSM connection and client certificate for the on-premises HSM. Enable HSM encryption on the existing unencrypted cluster by modifying the cluster. Connect to the VPC where the Amazon Redshift cluster resides from the on-premises network using a VPN.
D. Create a replica of the on-premises HSM in AWS CloudHSM. Launch a cluster in a VPC with the option to use CloudHSM to store keys.

A company currently uses Amazon Athena to query its global datasets. The regional data is stored in Amazon S3 in the us-east-1 and us-west-2 Regions. The data is not encrypted. To simplify the query process and manage it centrally, the company wants to use Athena in us-west-2 to query data from Amazon S3 in both Regions. The solution should be as low-cost as possible.
What should the company do to achieve this goal?

A. Use AWS DMS to migrate the AWS Glue Data Catalog from us-east-1 to us-west-2. Run Athena queries in us-west-2.
B. Run the AWS Glue crawler in us-west-2 to catalog datasets in all Regions. Once the data is crawled, run Athena queries in us-west-2.
C. Enable cross-Region replication for the S3 buckets in us-east-1 to replicate data in us-west-2. Once the data is replicated in us-west-2, run the AWS Glue crawler there to update the AWS Glue Data Catalog in us-west-2 and run Athena queries.
D. Update AWS Glue resource policies to provide us-east-1 AWS Glue Data Catalog access to us-west-2. Once the catalog in us-west-2 has access to the catalog in us-east-1, run Athena queries in us-west-2.

A hospital uses an electronic health records (EHR) system to collect two types of data:
– Patient information, which includes a patient's name and address.
– Diagnostic tests conducted and the results of these tests.
Patient information is expected to change periodically. Existing diagnostic test data never changes and only new records are added.
The hospital runs an Amazon Redshift cluster with four dc2.large nodes and wants to automate the ingestion of the patient information and diagnostic test data into respective Amazon Redshift tables for analysis. The EHR system exports data as CSV files to an Amazon S3 bucket on a daily basis. Two sets of CSV files are generated. One set of files is for patient information with updates, deletes, and inserts. The other set of files is for new diagnostic test data only.
What is the MOST cost-effective solution to meet these requirements?

A. Use Amazon EMR with Apache Hudi. Run daily ETL jobs using Apache Spark and the Amazon Redshift JDBC driver.
B. Use an AWS Glue crawler to catalog the data in Amazon S3. Use Amazon Redshift Spectrum to perform scheduled queries of the data in Amazon S3 and ingest the data into the patient information table and the diagnostic tests table.
C. Use an AWS Lambda function to run a COPY command that appends new diagnostic test data to the diagnostic tests table. Run another COPY command to load the patient information data into the staging tables. Use a stored procedure to handle create, update, and delete operations for the patient information table.
D. Use AWS Database Migration Service (AWS DMS) to collect and process change data capture (CDC) records. Use the COPY command to load patient information data into the staging tables. Use a stored procedure to handle create, update, and delete operations for the patient information table.

A company has an application that uses the Amazon Kinesis Client Library (KCL) to read records from a Kinesis data stream.
After a successful marketing campaign, the application experienced a significant increase in usage. As a result, a data analyst had to split some shards in the data stream. When the shards were split, the application started throwing an ExpiredlteratorExceptions error sporadically.
What should the data analyst do to resolve this?

A. Increase the number of threads that process the stream records.
B. Increase the provisioned read capacity units assigned to the stream's Amazon DynamoDB table.
C. Increase the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.
D. Decrease the provisioned write capacity units assigned to the stream's Amazon DynamoDB table.

An Amazon Redshift database contains sensitive user data. Logging is necessary to meet compliance requirements. The logs must contain database authentication attempts, connections, and disconnections. The logs must also contain each query run against the database and record which database user ran each query.
Which steps will create the required logs?

A. Enable Amazon Redshift Enhanced VPC Routing. Enable VPC Flow Logs to monitor traffic.
B. Allow access to the Amazon Redshift database using AWS 1AM only. Log access using AWS CloudTrail.
C. Enable audit logging for Amazon Redshift using the AWS Management Console or the AWS CLI.
D. Enable and download audit reports from AWS Artifact.