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Amazon Web Services Data-Engineer-Associate Dumps Questions Answers

Data-Engineer-Associate exam

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AWS Certified Data Engineer - Associate (DEA-C01)

Last Update Feb 19, 2026
Total Questions : 241 With Comprehensive Analysis

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Amazon Web Services Data-Engineer-Associate Exam Dumps FAQs

The Data-Engineer-Associate AWS Certified Associate exam consists of multiple-choice and multiple-response questions. The exam duration is 130 minutes, and the passing score is 720 out of 1000.
The Data-Engineer-Associate AWS Certified Associate exam covers data storage, processing, security, and analysis using AWS tools. It also tests your ability to design, optimize, and automate data workflows.
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The Amazon Web Services Data-Engineer-Associate (D-Associate) and the Amazon Web Services Machine Learning Engineer - Associate (MLA-C01) exams are both designed for professionals working with AWS, but they focus on different areas of expertise:

  • Amazon Web Services Data-Engineer-Associate Exam: The Amazon Web Services Data-Engineer-Associate Exam focuses on designing, building, and maintaining data solutions using AWS services. It covers data storage, data processing, data lakes, data pipelines, and data migration.
  • Amazon Web Services MLA-C01 Exam: The Amazon Web Services MLA-C01 Exam emphasizes implementing and operationalizing machine learning workloads on AWS. It covers data preparation, model development, deployment, orchestration of ML workflows, and monitoring ML solutions.
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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

Questions 1

A data engineer is building a data pipeline. A large data file is uploaded to an Amazon S3 bucket once each day at unpredictable times. An AWS Glue workflow uses hundreds of workers to process the file and load the data into Amazon Redshift. The company wants to process the file as quickly as possible.

Which solution will meet these requirements?

Options:

A.

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Lambda function that runs every 15 minutes to check the S3 bucket for the daily file. Configure the function to start the AWS Glue workflow if the file is present.

B.

Create an event-based AWS Glue trigger to start the workflow. Configure Amazon S3 to log events to AWS CloudTrail. Create a rule in Amazon EventBridge to forward PutObject events to the AWS Glue trigger.

C.

Create a scheduled AWS Glue trigger to start the workflow. Create a cron job that runs the AWS Glue job every 15 minutes. Set up the AWS Glue job to check the S3 bucket for the daily file. Configure the job to stop if the file is not present.

D.

Create an on-demand AWS Glue trigger to start the workflow. Create an AWS Database Migration Service (AWS DMS) migration task. Set the DMS source as the S3 bucket. Set the target endpoint as the AWS Glue workflow.

Questions 2

A company wants to implement real-time analytics capabilities. The company wants to use Amazon Kinesis Data Streams and Amazon Redshift to ingest and process streaming data at the rate of several gigabytes per second. The company wants to derive near real-time insights by using existing business intelligence (BI) and analytics tools.

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

Options:

A.

Use Kinesis Data Streams to stage data in Amazon S3. Use the COPY command to load data from Amazon S3 directly into Amazon Redshift to make the data immediately available for real-time analysis.

B.

Access the data from Kinesis Data Streams by using SQL queries. Create materialized views directly on top of the stream. Refresh the materialized views regularly to query the most recent stream data.

C.

Create an external schema in Amazon Redshift to map the data from Kinesis Data Streams to an Amazon Redshift object. Create a materialized view to read data from the stream. Set the materialized view to auto refresh.

D.

Connect Kinesis Data Streams to Amazon Kinesis Data Firehose. Use Kinesis Data Firehose to stage the data in Amazon S3. Use the COPY command to load the data from Amazon S3 to a table in Amazon Redshift.

Questions 3

A data engineer needs to maintain a central metadata repository that users access through Amazon EMR and Amazon Athena queries. The repository needs to provide the schema and properties of many tables. Some of the metadata is stored in Apache Hive. The data engineer needs to import the metadata from Hive into the central metadata repository.

Which solution will meet these requirements with the LEAST development effort?

Options:

A.

Use Amazon EMR and Apache Ranger.

B.

Use a Hive metastore on an EMR cluster.

C.

Use the AWS Glue Data Catalog.

D.

Use a metastore on an Amazon RDS for MySQL DB instance.

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