DEA-C01 Data Ingestion and Transformation Practice Question
Which TWO actions can improve the performance of an AWS Glue ETL job that processes large datasets in Amazon S3? (Choose two.)
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
✓
Increase the number of DPUs allocated to the job.
Increasing the number of DPUs allocates more processing power to the Glue job, which can speed up data processing for large datasets. Option D is correct because columnar file formats like Parquet or ORC are more efficient for analytical queries, reduce I/O, and allow better compression compared to row-based formats. Option A is incorrect: increasing crawler frequency only affects the metadata catalog update frequency, not the ETL job performance. Option B is incorrect: using a single Availability Zone for the S3 bucket does not improve performance and may reduce availability. Option E is incorrect: using a single large file can reduce parallelism, as distributed processing benefits from splitting data into multiple files to be processed in parallel by different executors.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the frequency of the Glue crawler.
Why it's wrong here
Crawler frequency does not impact ETL job performance.
- ✗
Use a single Availability Zone for the S3 bucket.
Why it's wrong here
S3 is regional; AZ does not apply.
- ✓
Increase the number of DPUs allocated to the job.
Why this is correct
More DPUs increase parallelism and memory.
- ✓
Use columnar file formats like Parquet or ORC.
Why this is correct
Columnar formats reduce I/O and improve compression.
- ✗
Use a single large file instead of many small files.
Why it's wrong here
Many small files can cause overhead; but single file limits parallelism.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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