DEA-C01 AWS Glue DPU (Data Processing Unit) Practice Question
A data engineer is monitoring an AWS Glue ETL job that processes data from an S3 bucket and writes to a Redshift table. The job completes successfully but takes longer than expected. The engineer notices that the job uses 10 DPUs and the data size is 500 GB. The job runs in standard mode. Which change would MOST reduce job duration?
⚠ Common exam trap
Candidates may assume that increasing DPUs always helps, but for small datasets or I/O-bound jobs, diminishing returns occur. However, for large datasets like 500 GB in standard mode, increasing DPUs typically reduces duration linearly up to a point.
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 to 20.
Increasing the number of DPUs from 10 to 20 allows the job to process data in parallel, reducing execution time. AWS Glue standard mode scales linearly with DPUs for ETL jobs that are not I/O bound. In this case, with 500 GB of data and 10 DPUs, the job is likely CPU-bound and can benefit from additional parallelism. Option B is incorrect because using a smaller worker type (G.1X) reduces available memory and CPU, worsening performance. Option C is incorrect because changing the output from Parquet (columnar, compressed) to CSV (row-based, uncompressed) increases data size and I/O, slowing the job. Option D is incorrect because reducing partitions can cause data skew and reduce parallelism, increasing runtime.
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 number of DPUs to 20.
Why this is correct
More DPUs provide more parallelism, reducing job execution time.
- ✗
Use a smaller worker type like G.1X.
Why it's wrong here
Smaller workers reduce memory and compute per worker, potentially increasing duration.
- ✗
Change the output format from Parquet to CSV.
Why it's wrong here
CSV is larger and slower to process than Parquet.
- ✗
Reduce the number of partitions in the data.
Why it's wrong here
Fewer partitions can lead to fewer tasks and less parallelism, increasing run time.
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
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