Amazon Web Services Data-Engineer-Associate Practice Test Questions Answers
Exam Code: Data-Engineer-Associate
(Updated 152 Q&As with Explanation)
Exam Name: AWS Certified Data Engineer - Associate (DEA-C01)
Last Update: 26-Mar-2025
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Data-Engineer-Associate Overview
Amazon Web Services Data-Engineer-Associate Exam Overview
| Aspect | Details |
|---|---|
| Exam Name | AWS Certified Data Engineer – Associate Exam |
| Certification | AWS Certified Data Engineer – Associate |
| Duration | 180 minutes |
| Number of Questions | Approximately 65 questions |
| Exam Format | Multiple choice, multiple answers, and scenario-based questions |
| Passing Score | 70% or higher |
| Language | English, Japanese, Korean, and Simplified Chinese |
| Exam Mode | Online Proctored or In-Person Proctored |
| Prerequisites | 1. Understanding of AWS services 2. Hands-on experience with data lakes, ETL, and data pipelines 3. Knowledge of databases, storage, and big data processing tools |
| Topics Covered | 1. Data Collection 2. Data Storage and Management 3. Data Processing 4. Data Security and Compliance 5. Data Analytics and Reporting 6. Data Engineering on AWS Platform |
| Preparation Resources | 1. AWS Training and Certification Courses 2. AWS Whitepapers and Documentation 3. Practice exams and sample questions |
| Tools & Technologies Covered | 1. AWS Glue 2. Amazon Redshift 3. AWS Lambda 4. Amazon S3 5. Amazon EMR 6. AWS Kinesis 7. AWS RDS, Aurora, DynamoDB |
| Recommended Experience | 1-2 years of hands-on experience in data engineering tasks on AWS |
| Topics Breakdown (Approx. %) | - Data Collection & Ingestion: 20-25% - Data Storage & Management: 20-25% - Data Processing: 25-30% - Data Security & Compliance: 10-15% - Data Analytics & Reporting: 10-15% |
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Amazon Web Services Data-Engineer-Associate Exam Topics Breakdown
| Exam Section | Topics Covered | Approx. Percentage |
|---|---|---|
| 1. Data Collection & Ingestion | - Data ingestion using AWS services (Kinesis, SQS, AWS Glue) - Data streaming and batch ingestion - ETL processes - Data pipeline creation |
20-25% |
| 2. Data Storage & Management | - Data storage in Amazon S3, Redshift, RDS, DynamoDB, and other storage options - Data lake architecture - Data warehousing and management |
20-25% |
| 3. Data Processing | - Using AWS Glue, Lambda, EMR, and Kinesis for processing data - Transforming and cleansing data - Data wrangling and pipeline management |
25-30% |
| 4. Data Security & Compliance | - Ensuring data encryption in transit and at rest - Access control policies (IAM, AWS KMS) - GDPR and compliance requirements |
10-15% |
| 5. Data Analytics & Reporting | - Data analytics services like AWS Athena, Redshift, and QuickSight - Creating and managing data reports and dashboards |
10-15% |
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