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 only refreshes Data Catalog metadata; it does not affect ETL execution performance. It tempts because crawlers underpin Glue jobs, so more frequent runs appear beneficial, yet the lever for throughput is worker type, worker count and partitioning, not catalog refresh cadence.
- ✗
Use a single Availability Zone for the S3 bucket.
Why it's wrong here
S3 buckets are regional and automatically span Availability Zones; you cannot pin a bucket to one AZ, and doing so would not speed up Glue. It tempts because co-locating compute and storage reduces latency in other services, but S3's architecture makes this inapplicable.
- ✓
Increase the number of DPUs allocated to the job.
Why this is correct
Increasing DPUs adds parallel executors, so more partitions are processed concurrently during the shuffle and write stages. This directly addresses the stem's large-dataset constraint, where a single worker count throttles throughput. More DPUs raise aggregate memory and CPU, reducing spills to disk and shortening each stage's runtime.
- ✓
Use columnar file formats like Parquet or ORC.
Why this is correct
Columnar formats store data by column rather than row, so AWS Glue reads only the referenced columns and skips irrelevant data. This cuts I/O and shuffle volume when processing large S3 datasets, directly addressing the performance constraint in the stem.
- ✗
Use a single large file instead of many small files.
Why it's wrong here
Consolidating into one large file reduces parallelism, since Glue splits work by file; many small files are the actual problem. It is tempting because fewer objects feel tidier. Compaction into fewer, larger files helps, but a single file removes the split points Glue needs.
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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