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NEW QUESTION 28
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. Increase the number of shards in the stream using the UpdateShardCount API.B. Choose partition keys in a way that results in a uniform record distribution across shards.C. Increase the Kinesis Data Streams retention period to reduce throttling.D. Replace the Kinesis API-based data ingestion mechanism with Kinesis Agent.E. Customize the application code to include retry logic to improve performance.

Answer: A,C

 

NEW QUESTION 29
A banking company wants to collect large volumes of transactional data using Amazon Kinesis Data Streams for real-time analytics. The company uses PutRecord to send data to Amazon Kinesis, and has observed network outages during certain times of the day. The company wants to obtain exactly once semantics for the entire processing pipeline.
What should the company do to obtain these characteristics?

A. Design the data producer so events are not ingested into Kinesis Data Streams multiple times.B. Design the application so it can remove duplicates during processing be embedding a unique ID in each record.C. Rely on the exactly one processing semantics of Apache Flink and Apache Spark Streaming included in Amazon EMR.D. Rely on the processing semantics of Amazon Kinesis Data Analytics to avoid duplicate processing of events.

Answer: B

 

NEW QUESTION 30
A company analyzes historical data and needs to query data that is stored in Amazon S3. New data is generated daily as .csv files that are stored in Amazon S3. The company's analysts are using Amazon Athena to perform SQL queries against a recent subset of the overall dat a. The amount of data that is ingested into Amazon S3 has increased substantially over time, and the query latency also has increased.
Which solutions could the company implement to improve query performance? (Choose two.)

A. Use Athena to extract the data and store it in Apache Parquet format on a daily basis. Query the extracted data.B. Use MySQL Workbench on an Amazon EC2 instance, and connect to Athena by using a JDBC or ODBC connector. Run the query from MySQL Workbench instead of Athena directly.C. Run a daily AWS Glue ETL job to compress the data files by using the .gzip format. Query the compressed data.D. Run a daily AWS Glue ETL job to convert the data files to Apache Parquet and to partition the converted files. Create a periodic AWS Glue crawler to automatically crawl the partitioned data on a daily basis.E. Run a daily AWS Glue ETL job to compress the data files by using the .lzo format. Query the compressed data.

Answer: A,D

Explanation:
Reference:
https://aws.amazon.com/blogs/big-data/work-with-partitioned-data-in-aws-glue/

 

NEW QUESTION 31
A company leverages Amazon Athena for ad-hoc queries against data stored in Amazon S3. The company wants to implement additional controls to separate query execution and query history among users, teams, or applications running in the same AWS account to comply with internal security policies.
Which solution meets these requirements?

A. Create an Athena workgroup for each given use case, apply tags to the workgroup, and create an IAM policy using the tags to apply appropriate permissions to the workgroup.B. Create an AWS Glue Data Catalog resource policy for each given use case that grants permissions to appropriate individual IAM users, and apply the resource policy to the specific tables used by Athena.C. Create an S3 bucket for each given use case, create an S3 bucket policy that grants permissions to appropriate individual IAM users. and apply the S3 bucket policy to the S3 bucket.D. Create an IAM role for each given use case, assign appropriate permissions to the role for the given use case, and add the role to associate the role with Athena.

Answer: A

Explanation:
Explanation
https://docs.aws.amazon.com/athena/latest/ug/user-created-workgroups.html Amazon Athena Workgroups - A new resource type that can be used to separate query execution and query history between Users, Teams, or Applications running under the same AWS account
https://aws.amazon.com/about-aws/whats-new/2019/02/athena_workgroups/

 

NEW QUESTION 32
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