SAP-C02 Continuous Improvement for Existing Solutions Practice Question

A company has a data pipeline that uses AWS Glue to process large datasets in Amazon S3. The pipeline runs daily and takes over 12 hours to complete. The company wants to reduce the processing time. Which approach would be MOST effective?

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 Glue job.

Increasing the number of DPUs (data processing units) allocated to the Glue job allows for greater parallelism, which directly reduces processing time for CPU-bound or memory-bound workloads. Option A is incorrect because increasing the timeout does not improve performance; it only prevents the job from failing due to time limits. Option B is incorrect because S3 Transfer Acceleration speeds up data transfer to S3, not the processing within Glue. Option D is incorrect because while converting to Parquet can improve read performance and reduce data volume, it does not address the core processing bottleneck if the job is compute-intensive; the most effective immediate step is to increase DPUs.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

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Increase the Glue job timeout setting to 24 hours.
Why it's wrong here
Raising the timeout only permits the job to run longer; it does not reduce the 12 hours of processing. It is tempting because timeouts do cause premature job failures, and extending them is correct when a valid job is being killed before completion rather than needing to finish sooner.
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Enable S3 Transfer Acceleration on the source bucket.
Why it's wrong here
Transfer Acceleration speeds up uploads to S3 over long distances; it does nothing to Glue's extract, transform and load throughput, which is the 12-hour bottleneck. It is tempting because it genuinely accelerates cross-region S3 transfers, and would be correct if ingestion into the source bucket were the slow stage.
✓
Increase the number of DPUs allocated to the Glue job.
Why this is correct
Glue scales horizontally by adding DPUs, each providing processing capacity and memory. Increasing DPUs for this long-running job parallelises the work across more executors, cutting the 12-hour runtime, whereas other changes do not raise raw compute throughput.
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Convert the input data from CSV to Parquet format.
Why it's wrong here
Parquet's columnar layout and compression cut I/O, but Glue still processes the same volume within one job run, so the 12-hour wall clock barely moves. It would help when reducing storage cost or scan time for analytics queries.

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

SAP-C02 Continuous Improvement for Existing Solutions Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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