DEA-C01 Data Ingestion and Transformation Practice Question
A company uses AWS Glue to process streaming data from Amazon Kinesis Data Streams. The data is JSON formatted and includes a timestamp field. The company wants to partition the output in Amazon S3 by date and hour, and ensure exactly-once processing semantics. Which combination of configurations should be used?
⚠ Common exam trap
Candidates often confuse the checkpointing mechanism of AWS Glue (which uses S3) with the Kinesis Client Library (KCL) pattern (which uses DynamoDB), leading them to select option D or C, even though Glue streaming jobs do not support DynamoDB for checkpointing.
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
✓
Enable checkpointing in the AWS Glue streaming job and specify an S3 location for checkpoint data.
AWS Glue streaming jobs require checkpointing to track the progress of data consumption from Kinesis Data Streams and to ensure exactly-once processing semantics. By enabling checkpointing and specifying an S3 location, Glue periodically saves the state of processed records, allowing it to resume from the last committed offset in case of failures, thus preventing duplicates or data loss.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Disable checkpointing and use the 'exactly_once' delivery option in Kinesis Data Streams.
Why it's wrong here
Kinesis Data Streams does not have an 'exactly_once' delivery option; checkpointing is required.
- ✓
Enable checkpointing in the AWS Glue streaming job and specify an S3 location for checkpoint data.
Why this is correct
Glue streaming jobs support checkpointing to S3 for exactly-once processing.
- ✗
Use Amazon DynamoDB as a checkpoint store by configuring the Glue job with a DynamoDB connection.
Why it's wrong here
AWS Glue streaming jobs checkpoint to S3, not DynamoDB.
- ✗
Use Kinesis Client Library (KCL) checkpointing with a DynamoDB table.
Why it's wrong here
KCL checkpointing is for custom applications, not AWS Glue streaming ETL jobs.
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.