- A
Use Amazon Kinesis Data Streams to capture DynamoDB Streams changes.
Why wrong: This is for streaming, not for bulk export; also may not guarantee exactly-once for large backfills.
- B
Configure the Glue job to convert data to Parquet format.
Parquet is columnar and efficient for analytics.
- C
Use Amazon S3 Object Lambda to transform data on the fly.
Why wrong: S3 Object Lambda is for user-defined transformations on GET requests, not for ingestion pipelines.
- D
Enable job bookmarks in the Glue job to track processed items.
Ensures exactly-once processing by skipping already processed data.
- E
Use DynamoDB's export to S3 feature to get a full snapshot.
Efficiently exports large data to S3 without scanning the table.
Question 1,774 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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.)
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.
Option B is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Job bookmarks in AWS Glue (Option D) track processed items by persisting state information, such as the last processed sequence number from DynamoDB Streams, enabling incremental processing and preventing duplicates. DynamoDB's export to S3 feature (Option E) provides a full snapshot in DynamoDB JSON or Ion format, which can be used as an initial baseline, while subsequent incremental changes are handled via Glue job bookmarks to achieve exactly-once semantics.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Configure the Glue job to convert data to Parquet format. — Option B is correct because 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.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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