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Data-Engineer-Associate - AWS Certified Data Engineer - Associate (DEA-C01)

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Data-Engineer-Associate Questions and Answers

Question # 1

A company maintains multiple extract, transform, and load (ETL) workflows that ingest data from the company's operational databases into an Amazon S3 based data lake. The ETL workflows use AWS Glue and Amazon EMR to process data.

The company wants to improve the existing architecture to provide automated orchestration and to require minimal manual effort.

Which solution will meet these requirements with the LEAST operational overhead?


AWS Glue workflows


AWS Step Functions tasks


AWS Lambda functions


Amazon Managed Workflows for Apache Airflow (Amazon MWAA) workflows

Question # 2

A healthcare company uses Amazon Kinesis Data Streams to stream real-time health data from wearable devices, hospital equipment, and patient records.

A data engineer needs to find a solution to process the streaming data. The data engineer needs to store the data in an Amazon Redshift Serverless warehouse. The solution must support near real-time analytics of the streaming data and the previous day's data.

Which solution will meet these requirements with the LEAST operational overhead?


Load data into Amazon Kinesis Data Firehose. Load the data into Amazon Redshift.


Use the streaming ingestion feature of Amazon Redshift.


Load the data into Amazon S3. Use the COPY command to load the data into Amazon Redshift.


Use the Amazon Aurora zero-ETL integration with Amazon Redshift.

Question # 3

A data engineer runs Amazon Athena queries on data that is in an Amazon S3 bucket. The Athena queries use AWS Glue Data Catalog as a metadata table.

The data engineer notices that the Athena query plans are experiencing a performance bottleneck. The data engineer determines that the cause of the performance bottleneck is the large number of partitions that are in the S3 bucket. The data engineer must resolve the performance bottleneck and reduce Athena query planning time.

Which solutions will meet these requirements? (Choose two.)


Create an AWS Glue partition index. Enable partition filtering.


Bucketthe data based on a column thatthe data have in common in a WHERE clause of the user query


Use Athena partition projection based on the S3 bucket prefix.


Transform the data that is in the S3 bucket to Apache Parquet format.


Use the Amazon EMR S3DistCP utility to combine smaller objects in the S3 bucket into larger objects.

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