DEA-C01 Data Ingestion and Transformation Practice Question
A data engineer runs an AWS Glue Studio job that reads JSON from Amazon S3 and writes to a partitioned Parquet table. The job currently runs for six hours. Profiling shows that a small number of partitions contain millions of rows while most contain a few hundred. Which change will MOST improve runtime?
⚠ Common exam trap
The trap here is assuming that more DPUs or bigger workers will fix any slow job, when skew requires redistributing data rather than adding compute.
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
✓
Apply a salting technique to the skewed keys before the write, or repartition on a composite key.
Data skew, not compute capacity, is causing the long runtime because a few partitions hold most of the rows. Salting hot keys or repartitioning on a composite key spreads those rows across many tasks so no single task becomes a straggler. Increasing DPUs, changing worker type, or enabling bookmarks does not change how rows are distributed within a run.
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 allocated to the Glue job from 10 to 100.
Why it's wrong here
Adding DPUs increases parallelism, but skewed partitions still funnel millions of rows into a few tasks that run long after the others finish. The bottleneck is data distribution, not total compute, so scaling out wastes money on idle workers while the stragglers persist.
- ✓
Apply a salting technique to the skewed keys before the write, or repartition on a composite key.
Why this is correct
Salting appends a random suffix to hot keys so rows spread across many tasks, and repartitioning on a composite key achieves similar balance. Both directly address the uneven task durations causing the long tail. This is the standard remedy for data skew in Spark and therefore in AWS Glue jobs.
- ✗
Enable job bookmarks and re-run the job to skip already-processed files.
Why it's wrong here
Job bookmarks prevent reprocessing old data on subsequent runs, which helps incremental pipelines. They do not redistribute rows within a single run, so the skew that causes the six-hour runtime remains untouched. This is a state-management feature, not a skew remedy.
- ✗
Switch the job's worker type from G.1X to G.2X to get more memory per executor.
Why it's wrong here
G.2X workers give more memory and vCPUs per worker, which can help memory pressure but does nothing for skewed key distribution. A few oversized partitions will still be processed by a small number of tasks, so the straggler effect and the six-hour runtime persist despite the larger instance size.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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