[NEW] [Practice Exams] AWS Certified Data Engineer Associate

[NEW] [Practice Exams] AWS Certified Data Engineer Associate free download

Pass in AWS Certified DEA-C01 exam! Study with 390 Practice Questions + Objective explanations for each answer!

Master the AWS Certified Data Engineer – Associate (DEA-C01) Exam

Are you ready to master data engineering on AWS and earn the prestigious AWS Certified Data Engineer – Associate (DEA-C01) certification? This course offers a complete preparation experience, featuring 6 full-length practice exams, detailed explanations, and real-world application scenarios to help you pass the exam with confidence.

Each question has been carefully crafted to reflect the exam’s tone, format, and difficulty level. With technical glossaries and practical context, this course ensures you're not just memorizing answers—but truly understanding AWS data engineering services and best practices.

Why Choose This Course?

6 Full-Length Practice Tests
Simulate real exam conditions with questions designed to match the complexity and format of the official DEA-C01 certification exam.

Detailed Answer Explanations
Every question includes a breakdown of correct and incorrect options—paired with official AWS concepts and service documentation references.

Glossary of Key Terms
Each question comes with a curated glossary, clarifying technical terms like partition projection, data lake formation, streaming ingestion, and S3 object versioning.

Real-World Use Cases
Bridge theory and practice with real-life applications, making your learning process intuitive and memorable.

Unlimited Retakes and Mobile Access
Practice anytime, anywhere with unlimited access via the Udemy app.

Instructor Support + 30-Day Guarantee
Ask questions and receive direct support from an AWS-certified instructor. Not satisfied? Get your money back—no questions asked.

What You’ll Learn

• Build modern data pipelines using AWS Glue, Lake Formation, and Kinesis

• Design scalable data lake and warehouse architectures using S3, Redshift, and Athena

• Optimize data transformation and querying processes with best practices

• Apply governance using IAM, encryption, object versioning, and fine-grained access control

• Confidently prepare to pass the DEA-C01 exam on your first try

Sample Question

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Question:
A data engineering team is storing raw machine learning datasets in Amazon S3 and needs to enable versioning to support data reproducibility and rollback. Which AWS service provides the most suitable solution for managing versioned raw datasets?

Option 1: Use Amazon S3 with versioning enabled to store raw datasets
Explanation: Correct. Amazon S3 supports native object versioning, allowing data engineers to track changes, enable rollback, and ensure reproducibility across ML pipelines.

Option 2: Use AWS Glue as a data catalog for raw datasets with version control
Explanation: Incorrect. AWS Glue catalogs data but does not offer native versioning for stored datasets.

Option 3: Use Amazon Redshift for raw dataset storage and versioning
Explanation: Incorrect. Redshift is optimized for structured analytical queries, not for raw data versioning or large-scale storage.

Option 4: Use SageMaker Feature Store to manage raw dataset versions
Explanation: Incorrect. SageMaker Feature Store is designed to store engineered features for model training/inference—not raw datasets.

Glossary

• Amazon S3 Versioning: Enables multiple versions of an object to be stored, retrieved, and protected.

• AWS Glue: Managed ETL service for data transformation and metadata cataloging, but not storage versioning.

• SageMaker Feature Store: Repository for engineered ML features, not raw data management.

• Redshift: Columnar data warehouse optimized for complex queries, not versioned storage.

• Data Reproducibility: Ensures the same data can be retrieved or restored for consistent ML training results.

How This Applies in the Real World

In real ML and data engineering pipelines, raw data is continuously updated or appended. Data engineers use Amazon S3 with versioning to ensure they can reproduce past model training runs, recover from accidental overwrites, or trace drift across datasets.

For example, a team building fraud detection models may store daily logs in an S3 bucket with versioning enabled. This allows them to retrain models using historical snapshots or roll back to a previous dataset if a new ingestion introduces inconsistencies.

This practice also supports compliance and auditing, giving organizations visibility and control over data changes—crucial in regulated industries.

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Benefits of AWS Certified Data Engineer – Associate Certification

• Advance Your Career: Open doors to cloud-focused roles in data engineering and analytics.

• Increase Your Earning Potential: Validate your AWS expertise and stand out in competitive markets.

• Gain Industry Recognition: Join a globally respected community of AWS Certified Data Engineers.

Don’t just aim to pass—master the material. Enroll today and take the next step toward becoming a certified AWS Data Engineer!