DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer is building a data ingestion pipeline using AWS Glue. The source is an Amazon DynamoDB table, and the target is an Amazon S3 data lake in Parquet format. The pipeline must handle large volumes and ensure exactly-once processing. Which THREE features should the engineer use together to achieve this? (Choose THREE.)
⚠ Common exam trap
Many candidates confuse streaming services like Kinesis Data Streams with batch processing, assuming they are required for exactly-once guarantees, when in fact AWS Glue job bookmarks combined with DynamoDB Streams or export to S3 provide a simpler and more reliable solution.
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
✓
Configure the Glue job to convert data to Parquet format.
Converting data to Parquet format is a core requirement for an S3 data lake, as Parquet offers columnar storage, compression, and efficient querying via services like Amazon Athena and Amazon Redshift Spectrum. AWS Glue natively supports Parquet as an output format, enabling the engineer to specify it in the job's output schema or transformation logic.
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 Amazon Kinesis Data Streams to capture DynamoDB Streams changes.
Why it's wrong here
This is for streaming, not for bulk export; also may not guarantee exactly-once for large backfills.
- ✓
Configure the Glue job to convert data to Parquet format.
Why this is correct
Parquet is columnar and efficient for analytics.
- ✗
Use Amazon S3 Object Lambda to transform data on the fly.
Why it's wrong here
S3 Object Lambda is for user-defined transformations on GET requests, not for ingestion pipelines.
- ✓
Enable job bookmarks in the Glue job to track processed items.
Why this is correct
Ensures exactly-once processing by skipping already processed data.
- ✓
Use DynamoDB's export to S3 feature to get a full snapshot.
Why this is correct
Efficiently exports large data to S3 without scanning the table.
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.