Free Amazon Data-Engineer-Associate Exam Questions
Absolute Free Data-Engineer-Associate Exam Practice for Comprehensive Preparation
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Amazon Data-Engineer-Associate Exam Questions
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Provided By: Amazon
- Exam: AWS Certified Data Engineer Associate (DEA-C01)
- Certification: AWS Certified Associate
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Total Questions: 582
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Updated On: Jan 14, 2026
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Rated: 4.9 |
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Online Users: 1164
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Question 1
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A photography studio stores its high-resolution image files in an Amazon S3 bucket. Recently, they encountered issues with accidental overwrites of important files. The studio requires a solution to prevent the overwriting and accidental deletion of their images while ensuring that the latest version of a file is readily accessible.
Which single feature should the photography studio enable on their S3 bucket to address this issue effectively?
Answer: A
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A photography studio stores its high-resolution image files in an Amazon S3 bucket. Recently, they encountered issues with accidental overwrites of important files. The studio requires a solution to prevent the overwriting and accidental deletion of their images while ensuring that the latest version of a file is readily accessible.
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Question 2
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A Data Engineering Team at a financial services company is developing a data API to serve real-time, user-specific transactional data from an Amazon RDS for PostgreSQL database to their mobile banking application.
The data is highly dynamic, with frequent reads and writes. The API must offer low latency and high availability, and be capable of scaling automatically to handle peak loads during business hours.
Given these requirements, which architecture should the team implement?
Answer: D
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A Data Engineering Team at a financial services company is developing a data API to serve real-time, user-specific transactional data from an Amazon RDS for PostgreSQL database to their mobile banking application.
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Question 3
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A data engineer is designing a new data lake architecture for a company. The data engineer plans to useApache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadatahandling. The data engineer needs to query historical data for trend analysis and optimize storage costs for alarge volume of event data.Which solution will meet these requirements with the LEAST development effort?
Answer: A
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A data engineer is designing a new data lake architecture for a company. The data engineer plans to useApache Iceberg tables and AWS Glue Data Catalog to achieve fast query performance and enhanced metadatahandling. The data engineer needs to query historical data for trend analysis and optimize storage costs for alarge volume of event data.Which solution will meet these requirements with the LEAST development effort?
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Question 4
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A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company
has an ecommerce application that generates a dataset that contains personally identifiable information (PII).
The company has an internal analytics application that does not require access to the PII.
To comply with regulations, the company must not share PII unnecessarily. A data engineer needs to
implement a solution that with redact PII dynamically, based on the needs of each application that accesses the
dataset.
Which solution will meet the requirements with the LEAST operational overhead?
Answer: B
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A company has multiple applications that use datasets that are stored in an Amazon S3 bucket. The company
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Question 5
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A company uses an Amazon S3 bucket to store data in both JSON and .csv formats. They also operate databases on Amazon RDS for Microsoft SQL Server, have Amazon DynamoDB tables in provisioned capacity mode, and run queries on an Amazon Redshift cluster. They need a straightforward solution that allows their data scientists to perform SQL-like queries across all these data sources.
What is the simplest solution for enabling these queries with the least operational overhead?
Answer: A
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A company uses an Amazon S3 bucket to store data in both JSON and .csv formats. They also operate databases on Amazon RDS for Microsoft SQL Server, have Amazon DynamoDB tables in provisioned capacity mode, and run queries on an Amazon Redshift cluster. They need a straightforward solution that allows their data scientists to perform SQL-like queries across all these data sources.