DEA-C01 Data Operations and Support Practice Question
A data engineer is optimizing an AWS Glue ETL job that processes large Parquet files in Amazon S3. The job currently takes several hours to complete. The engineer wants to improve performance by tuning the job's execution parameters. Which TWO actions will MOST effectively reduce the job's runtime? (Choose two.)
⚠ Common exam trap
A common mix-up: candidates confuse features that improve incremental processing (like job bookmarks) with those that improve single-run performance, and assuming that DynamicFrames are always faster.
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
✓
Partition the input data in Amazon S3 and use predicate pushdown in the Glue job.
To reduce the runtime of a Glue ETL job processing large Parquet files, increasing DPUs provides more compute resources for parallel processing, and partitioning the input data with predicate pushdown reduces the amount of data read. These two actions directly address performance bottlenecks. Job bookmarks, DynamicFrames, and timeout adjustments do not effectively reduce runtime for a single large job 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.
- ✓
Partition the input data in Amazon S3 and use predicate pushdown in the Glue job.
Why this is correct
Partitioning the input data (e.g., by date or category) allows Glue to read only relevant partitions, reducing I/O. Predicate pushdown pushes filter conditions to the data source, so only matching data is read. Together, they minimize the amount of data scanned and processed, significantly improving runtime for large datasets. This is a best practice for optimizing Glue ETL jobs.
- ✗
Use the Glue DynamicFrame instead of Spark DataFrame for all transformations.
Why it's wrong here
Glue DynamicFrames are convenient but often slower than native Spark DataFrames because they add abstraction and may not leverage all Spark optimizations. Converting to Spark DataFrames can improve performance. Therefore, using DynamicFrames exclusively would not reduce runtime; it could increase it. This option is counterproductive for performance tuning.
- ✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
Increasing DPUs provides more compute resources, which can parallelize data processing and reduce runtime for large datasets. Glue distributes work across executors; more DPUs mean more executors, leading to faster processing. However, there are diminishing returns if the job is I/O-bound or if the data is skewed. For large Parquet files, additional DPUs can significantly speed up transformations and writes.
- ✗
Increase the Glue job's timeout value to allow more time for completion.
Why it's wrong here
Increasing the timeout does not speed up the job; it only allows the job to run longer before being terminated. If the job is already taking hours, a longer timeout might prevent failures, but it does not reduce runtime. The goal is to reduce runtime, not just avoid timeouts. This option addresses a different problem.
- ✗
Enable job bookmarks to track processed data.
Why it's wrong here
Job bookmarks help process only new data in incremental runs, reducing the amount of data processed on subsequent runs. However, for a single job run over a large existing dataset, bookmarks do not reduce runtime; they only prevent reprocessing already-processed data. The scenario describes a one-time optimization of a job that processes large files, so bookmarks are not effective for reducing the current runtime.
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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