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Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Exam Code:
Data-Engineer-Associate
Exam Name:
AWS Certified Data Engineer - Associate (DEA-C01)
Certification:
AWS Certified Data Engineer
Vendor:
Amazon Web Services
Questions:
218
Last Updated:
Dec 8, 2025
Exam Status:
Stable
Amazon Web Services Data-Engineer-Associate

Data-Engineer-Associate: AWS Certified Data Engineer Exam 2025 Study Guide Pdf and Test Engine

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AWS Certified Data Engineer - Associate (DEA-C01) Questions and Answers

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Question 1

A media company uses software as a service (SaaS) applications to gather data by using third-party tools. The company needs to store the data in an Amazon S3 bucket. The company will use Amazon Redshift to perform analytics based on the data.

Which AWS service or feature will meet these requirements with the LEAST operational overhead?

Options:

A.

Amazon Managed Streaming for Apache Kafka (Amazon MSK)

B.

Amazon AppFlow

C.

AWS Glue Data Catalog

D.

Amazon Kinesis

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Question 2

A technology company currently uses Amazon Kinesis Data Streams to collect log data in real time. The company wants to use Amazon Redshift for downstream real-time queries and to enrich the log data.

Which solution will ingest data into Amazon Redshift with the LEAST operational overhead?

Options:

A.

Set up an Amazon Data Firehose delivery stream to send data to a Redshift provisioned cluster table.

B.

Set up an Amazon Data Firehose delivery stream to send data to Amazon S3. Configure a Redshift provisioned cluster to load data every minute.

C.

Configure Amazon Managed Service for Apache Flink (previously known as Amazon Kinesis Data Analytics) to send data directly to a Redshift provisioned cluster table.

D.

Use Amazon Redshift streaming ingestion from Kinesis Data Streams and to present data as a materialized view.

Question 3

A data engineer is processing a large amount of log data from web servers. The data is stored in an Amazon S3 bucket. The data engineer uses AWS services to process the data every day. The data engineer needs to extract specific fields from the raw log data and load the data into a data warehouse for analysis.

Options:

A.

Use Amazon EMR to run Apache Hive queries on the raw log files in the S3 bucket to extract the specified fields. Store the output as ORC files in the original S3 bucket.

B.

Use AWS Step Functions to orchestrate a series of AWS Batch jobs to parse the raw log files. Load the specified fields into an Amazon RDS for PostgreSQL database.

C.

Use an AWS Glue crawler to parse the raw log data in the S3 bucket and to generate a schema. Use AWS Glue ETL jobs to extract and transform the data and to load it into Amazon Redshift.

D.

Use AWS Glue DataBrew to run AWS Glue ETL jobs on a schedule to extract the specified fields from the raw log files in the S3 bucket. Load the data into partitioned tables in Amazon Redshift.

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