DEA-C01 Data Operations and Support Practice Question

A company is using AWS Glue to process data stored in Amazon S3. The Glue job runs successfully but takes longer than expected. Which TWO actions can reduce the job runtime?

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 job

Option B is correct because increasing the number of DPUs (Data Processing Units) allocated to a Glue job adds more compute capacity and parallelism, allowing Spark executors to process partitions concurrently and thereby reducing overall runtime. Option E is correct because partitioning the input data in S3 (for example, by date or category) lets Glue's Spark engine prune irrelevant partitions and read only the data it needs, cutting I/O and shuffle overhead. Option A is wrong because disabling job bookmarks causes Glue to reprocess already-processed data, which typically increases runtime rather than reducing it. Option C is wrong because reducing the number of workers lowers parallelism and generally makes the job slower. Option D is wrong because Python shell jobs are single-node, non-distributed, and intended for lightweight scripts, so they cannot efficiently process large datasets that a distributed Spark job handles.

Answer analysis

Option-by-option breakdown

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

✗
Disable job bookmarking
Why it's wrong here
Disabling job bookmarking forces Glue to reprocess every S3 object on each run, increasing runtime rather than reducing it. Bookmarks exist to track previously processed data so incremental jobs skip unchanged files; disabling them suits full reprocessing after schema changes, not performance tuning.
✓
Increase the number of DPUs allocated to the job
Why this is correct
Glue allocates DPUs per job, and each DPU supplies processing capacity and memory. Raising the DPU count lets more executors run partitions concurrently, reducing runtime for large shuffles or skewed workloads, provided the data is partitioned into enough input splits.
✗
Reduce the number of workers
Why it's wrong here
Fewer workers reduce the total number of executors available for parallel task execution, so the job processes partitions more slowly. Worker count controls horizontal compute capacity; reducing it suits cost-constrained workloads with small datasets, not a job whose runtime must be shortened.
✗
Change the job type from Spark to Python shell
Why it's wrong here
Python shell jobs run single-node scripts and cannot execute distributed Spark transformations, so a Spark ETL workload would fail or need rewriting. Python shell suits lightweight, small-scale jobs that do not require parallel processing across a cluster.
✓
Partition the input data in S3
Why this is correct
Partitioning the S3 input by commonly filtered keys lets Glue prune irrelevant partitions before reading, so each job reads only the required subset of objects rather than scanning the entire dataset. This directly cuts the input volume that drives the excessive 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

Go deeper

DEA-C01 Data Operations and Support Practice Question

Why each option matters

Option-by-option breakdown

AWS S3 Storage Class Comparison

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