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NEW QUESTION 21
A company launched a service that produces millions of messages every day and uses Amazon Kinesis Data Streams as the streaming service.
The company uses the Kinesis SDK to write data to Kinesis Data Streams. A few months after launch, a data analyst found that write performance is significantly reduced. The data analyst investigated the metrics and determined that Kinesis is throttling the write requests. The data analyst wants to address this issue without significant changes to the architecture.
Which actions should the data analyst take to resolve this issue? (Choose two.)

• A. Choose partition keys in a way that results in a uniform record distribution across shards.
• B. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.
• C. Customize the application code to include retry logic to improve performance.
• D. Increase the number of shards in the stream using the UpdateShardCount API.
• E. Increase the Kinesis Data Streams retention period to reduce throttling.

Answer: A,D

Explanation:
Explanation
https://aws.amazon.com/blogs/big-data/under-the-hood-scaling-your-kinesis-data-streams/

NEW QUESTION 22
A marketing company is using Amazon EMR clusters for its workloads. The company manually installs third- party libraries on the clusters by logging in to the master nodes. A data analyst needs to create an automated solution to replace the manual process.
Which options can fulfill these requirements? (Choose two.)

• A. Place the required installation scripts in Amazon S3 and execute them using custom bootstrap actions.
• B. Place the required installation scripts in Amazon S3 and execute them through Apache Spark in Amazon EMR.
• C. Install the required third-party libraries in the existing EMR master node. Create an AMI out of that master node and use that custom AMI to re-create the EMR cluster.
• D. Use an Amazon DynamoDB table to store the list of required applications. Trigger an AWS Lambda function with DynamoDB Streams to install the software.
• E. Launch an Amazon EC2 instance with Amazon Linux and install the required third-party libraries on the instance. Create an AMI and use that AMI to create the EMR cluster.

Answer: A,E

Explanation:
https://aws.amazon.com/about-aws/whats-new/2017/07/amazon-emr-now-supports-launching-clusters-with-custom-amazon-linux-amis/ https://docs.aws.amazon.com/de_de/emr/latest/ManagementGuide/emr-plan-bootstrap.html

NEW QUESTION 23
A company that produces network devices has millions of users. Data is collected from the devices on an hourly basis and stored in an Amazon S3 data lake.
The company runs analyses on the last 24 hours of data flow logs for abnormality detection and to troubleshoot and resolve user issues. The company also analyzes historical logs dating back 2 years to discover patterns and look for improvement opportunities.
The data flow logs contain many metrics, such as date, timestamp, source IP, and target IP. There are about 10 billion events every day.
How should this data be stored for optimal performance?

• A. In Apache ORC partitioned by date and sorted by source IP
• B. In Apache Parquet partitioned by source IP and sorted by date
• C. In compressed .csv partitioned by date and sorted by source IP
• D. In compressed nested JSON partitioned by source IP and sorted by date

Answer: D

NEW QUESTION 24
A company is hosting an enterprise reporting solution with Amazon Redshift. The application provides reporting capabilities to three main groups: an executive group to access financial reports, a data analyst group to run long-running ad-hoc queries, and a data engineering group to run stored procedures and ETL processes.
The executive team requires queries to run with optimal performance. The data engineering team expects queries to take minutes.
Which Amazon Redshift feature meets the requirements for this task?

• A. Concurrency scaling
• B. Short query acceleration (SQA)
• C. Workload management (WLM)
• D. Materialized views

Answer: D

Explanation:
Explanation
Materialized views:

NEW QUESTION 25
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