DEA-C01 Data Operations and Support Practice Question

An AWS Glue job that performs data transformation on large Parquet files in Amazon S3 is taking a long time to complete. The job uses the default number of DPUs. Which change would most likely improve the job's performance?

⚠ Common exam trap

DEA-C01 often tests the misconception that reducing output files or changing file formats improves Glue performance, when the real lever for compute-bound transformations is scaling DPUs and ensuring adequate partitioning.

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 'Max capacity' (number of DPUs) for the job.

AWS Glue allocates DPUs (Data Processing Units) to run the job, and increasing Max capacity adds more compute and memory resources that can parallelize the transformation across partitions. For large Parquet files with the default DPU count, the job is likely CPU- or memory-bound, so scaling DPUs is the most direct performance improvement. Parquet is already a columnar, compressed format, so the bottleneck is compute, not I/O format.

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 'Max capacity' (number of DPUs) for the job.
Why this is correct
The job is bottlenecked by compute, not partitioning or file size. Raising Max capacity adds more DPUs, distributing the Parquet transformation across additional executors and reducing wall-clock time, directly addressing the stem's constraint that the job runs on the default DPU allocation.
✗
Use 'coalesce' to reduce the number of output files.
Why it's wrong here
Coalesce merges output partitions after the shuffle, reducing small files written to S3 but leaving the transformation's shuffle and compute stages unchanged. It suits jobs producing many tiny output files. Here the bottleneck is processing throughput, addressed by allocating more DPUs.
✗
Reduce the number of partitions in the source data.
Why it's wrong here
Fewer source partitions reduces parallelism, so fewer tasks run concurrently across the default DPUs, lengthening the transformation. Reducing partitions suits small datasets where task overhead dominates. For large Parquet files, the constraint is compute capacity, so adding DPUs raises throughput.
✗
Change the input format from Parquet to CSV.
Why it's wrong here
CSV is row-based and uncompressed, so Glue reads and parses far more bytes than columnar Parquet, increasing I/O and CPU time. CSV suits interoperability with tools lacking Parquet support. Here the bottleneck is compute capacity, so the fix is allocating more DPUs.

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

DEA-C01 Data Operations and Support Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

Related to this question