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AWS Certified Data Analytics - Specialty

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AWS Certified Data Analytics - Specialty Questions and Answers

Questions 1

A retail company stores order invoices in an Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster Indices on the cluster are created monthly Once a new month begins, no new writes are made to any of the indices from the previous months The company has been expanding the storage on the Amazon OpenSearch Service {Amazon Elasticsearch Service) cluster to avoid running out of space, but the company wants to reduce costs Most searches on the cluster are on the most recent 3 months of data while the audit team requires infrequent access to older data to generate periodic reports The most recent 3 months of data must be quickly available for queries, but the audit team can tolerate slower queries if the solution saves on cluster costs

Which of the following is the MOST operationally efficient solution to meet these requirements?

Options:

A.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to store the indices in Amazon S3 Glacier When the audit team requires the archived data restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

B.

Archive indices that are older than 3 months by taking manual snapshots and storing the snapshots in Amazon S3 When the audit team requires the archived data, restore the archived indices back to the Amazon OpenSearch Service (Amazon Elasticsearch Service) cluster

C.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage

D.

Archive indices that are older than 3 months by using Index State Management (ISM) to create a policy to migrate the indices to Amazon OpenSearch Service (Amazon Elasticsearch Service) UltraWarm storage When the audit team requires the older data: migrate the indices in UltraWarm storage back to hot storage

Questions 2

An operations team notices that a few AWS Glue jobs for a given ETL application are failing. The AWS Glue jobs read a large number of small JSON files from an Amazon S3 bucket and write the data to a different S3 bucket in Apache Parquet format with no major transformations. Upon initial investigation, a data engineer notices the following error message in the History tab on the AWS Glue console: “Command Failed with Exit Code 1.”

Upon further investigation, the data engineer notices that the driver memory profile of the failed jobs crosses the safe threshold of 50% usage quickly and reaches 90–95% soon after. The average memory usage across all executors continues to be less than 4%.

The data engineer also notices the following error while examining the related Amazon CloudWatch Logs.

What should the data engineer do to solve the failure in the MOST cost-effective way?

Options:

A.

Change the worker type from Standard to G.2X.

B.

Modify the AWS Glue ETL code to use the ‘groupFiles’: ‘inPartition’ feature.

C.

Increase the fetch size setting by using AWS Glue dynamics frame.

D.

Modify maximum capacity to increase the total maximum data processing units (DPUs) used.

Questions 3

A software company hosts an application on AWS, and new features are released weekly. As part of the application testing process, a solution must be developed that analyzes logs from each Amazon EC2 instance to ensure that the application is working as expected after each deployment. The collection and analysis solution should be highly available with the ability to display new information with minimal delays.

Which method should the company use to collect and analyze the logs?

Options:

A.

Enable detailed monitoring on Amazon EC2, use Amazon CloudWatch agent to store logs in Amazon S3, and use Amazon Athena for fast, interactive log analytics.

B.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and visualize using Amazon QuickSight.

C.

Use the Amazon Kinesis Producer Library (KPL) agent on Amazon EC2 to collect and send data to Kinesis Data Firehose to further push the data to Amazon Elasticsearch Service and Kibana.

D.

Use Amazon CloudWatch subscriptions to get access to a real-time feed of logs and have the logs delivered to Amazon Kinesis Data Streams to further push the data to Amazon Elasticsearch Service and Kibana.