DEA-C01 Data Ingestion and Transformation Practice Question
A company is using AWS Glue to run ETL jobs that transform data from Amazon DynamoDB to Amazon S3. The DynamoDB table has a large number of items (over 10 million) and is heavily used by production applications. The Glue job reads the entire DynamoDB table each time it runs, causing increased read capacity consumption and affecting production performance. The team wants to reduce the impact on the source DynamoDB table while still keeping the S3 data up-to-date. What should the team do?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use DynamoDB Streams and AWS Lambda to capture changes and write them to S3, then run incremental Glue jobs.
Using DynamoDB Streams with AWS Lambda enables incremental change data capture (CDC), which eliminates the need to read the entire DynamoDB table each time. This reduces read capacity consumption and minimizes impact on production performance. Option B is incorrect because increasing read capacity units would still involve full table scans, further straining the production workload. Option C is incorrect because exporting via the DynamoDB console is a one-time export, not an incremental solution to keep S3 data up-to-date. Option D is incorrect because reducing Glue job parallelism does not change the fact that the entire table is read, and it would increase job duration without addressing the read capacity issue.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use DynamoDB Streams and AWS Lambda to capture changes and write them to S3, then run incremental Glue jobs.
Why this is correct
Captures only changes, reducing read impact.
- ✗
Increase the DynamoDB read capacity units to handle the Glue job's read load.
Why it's wrong here
Increases cost but still reads the entire table each time.
- ✗
Use the DynamoDB console to export the table to S3 in Parquet format.
Why it's wrong here
Export is one-time; does not keep data up-to-date.
- ✗
Reduce the parallelism of the Glue job to lower the read throughput.
Why it's wrong here
Reduces impact but still reads full table, just slower.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.