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NEW QUESTION 36
A smart home automation company must efficiently ingest and process messages from various connected devices and sensors. The majority of these messages are comprised of a large number of small files. These messages are ingested using Amazon Kinesis Data Streams and sent to Amazon S3 using a Kinesis data stream consumer application. The Amazon S3 message data is then passed through a processing pipeline built on Amazon EMR running scheduled PySpark jobs.
The data platform team manages data processing and is concerned about the efficiency and cost of downstream data processing. They want to continue to use PySpark.
Which solution improves the efficiency of the data processing jobs and is well architected?

• A. Launch an Amazon Redshift cluster. Copy the collected data from Amazon S3 to Amazon Redshift and move the data processing jobs from Amazon EMR to Amazon Redshift.
• B. Send the sensor and devices data directly to a Kinesis Data Firehose delivery stream to send the data to Amazon S3 with Apache Parquet record format conversion enabled. Use Amazon EMR running PySpark to process the data in Amazon S3.
• C. Set up an AWS Lambda function with a Python runtime environment. Process individual Kinesis data stream messages from the connected devices and sensors using Lambda.
• D. Set up AWS Glue Python jobs to merge the small data files in Amazon S3 into larger files and transform them to Apache Parquet format. Migrate the downstream PySpark jobs from Amazon EMR to AWS Glue.

Answer: B

NEW QUESTION 37
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?

• A. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
• B. Redeploy the EMR clusters that are running slowly to a different Availability Zone.
• C. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
• D. Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.

Answer: B

NEW QUESTION 38
A media company has been performing analytics on log data generated by its applications. There has been a recent increase in the number of concurrent analytics jobs running, and the overall performance of existing jobs is decreasing as the number of new jobs is increasing. The partitioned data is stored in Amazon S3 One Zone-Infrequent Access (S3 One Zone-IA) and the analytic processing is performed on Amazon EMR clusters using the EMR File System (EMRFS) with consistent view enabled. A data analyst has determined that it is taking longer for the EMR task nodes to list objects in Amazon S3.
Which action would MOST likely increase the performance of accessing log data in Amazon S3?

• A. Redeploy the EMR clusters that are running slowly to a different Availability Zone.
• B. Use a lifecycle policy to change the S3 storage class to S3 Standard for the log data.
• C. Increase the read capacity units (RCUs) for the shared Amazon DynamoDB table.
• D. Use a hash function to create a random string and add that to the beginning of the object prefixes when storing the log data in Amazon S3.

Answer: C

Explanation:
Explanation
https://docs.aws.amazon.com/emr/latest/ManagementGuide/emrfs-metadata.html

NEW QUESTION 39
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