- A
Reduce the number of DPUs to 5 and increase the Spark executor memory by setting 'spark.executor.memory' in job parameters.
Why wrong: Reducing DPUs reduces total memory, and executor memory cannot exceed total DPU memory.
- B
Increase the number of DPUs to 20 and enable job bookmarking for incremental processing.
More DPUs increase total available memory; job bookmarking reduces data volumes by processing only new data.
- C
Change the script to use DynamicFrame instead of DataFrame and disable the 'spark.sql.shuffle.partitions' configuration.
Why wrong: DynamicFrame does not inherently reduce memory usage; disabling shuffle partitions may cause out-of-memory errors.
- D
Add a 'coalesce(1)' operation before writing to Redshift to reduce the number of output files.
Why wrong: Coalescing to one partition forces all data into a single executor, worsening memory pressure.
DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company runs a nightly batch processing pipeline using AWS Glue ETL jobs. The pipeline reads data from an Amazon S3 bucket, transforms it, and writes results to an Amazon Redshift cluster. Recently, the data volume has increased significantly, and some Glue jobs are failing with the error 'java.lang.OutOfMemoryError: Java heap space'. The data engineer needs to modify the job configuration to prevent these failures without changing the code. The job currently uses 10 DPUs and processes data in a single Spark DataFrame. Which of the following is the MOST effective solution?
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 to 20 and enable job bookmarking for incremental processing.
Increasing DPUs from 10 to 20 provides more memory and compute resources, directly addressing the 'java.lang.OutOfMemoryError: Java heap space' caused by insufficient memory for the single DataFrame. Enabling job bookmarking allows incremental processing, which reduces the volume of data processed per run, further mitigating memory pressure without code changes.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Reduce the number of DPUs to 5 and increase the Spark executor memory by setting 'spark.executor.memory' in job parameters.
Why it's wrong here
Reducing DPUs reduces total memory, and executor memory cannot exceed total DPU memory.
- ✓
Increase the number of DPUs to 20 and enable job bookmarking for incremental processing.
Why this is correct
More DPUs increase total available memory; job bookmarking reduces data volumes by processing only new data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Change the script to use DynamicFrame instead of DataFrame and disable the 'spark.sql.shuffle.partitions' configuration.
Why it's wrong here
DynamicFrame does not inherently reduce memory usage; disabling shuffle partitions may cause out-of-memory errors.
- ✗
Add a 'coalesce(1)' operation before writing to Redshift to reduce the number of output files.
Why it's wrong here
Coalescing to one partition forces all data into a single executor, worsening memory pressure.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think reducing DPUs or using coalesce reduces memory usage, but in reality, both actions increase memory pressure on individual executors, making OOM errors more likely.
Detailed technical explanation
How to think about this question
AWS Glue allocates memory per DPU (1 DPU = 4 vCPU and 16 GB memory), so increasing DPUs scales both CPU and memory. Job bookmarking maintains state of processed S3 objects, allowing subsequent runs to skip already-processed data, which reduces the DataFrame size and memory footprint. The OOM error typically occurs during shuffles or when the DataFrame exceeds executor memory; more DPUs distribute the data across more executors, reducing per-executor load.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Operations and Support — This question tests Data Operations and Support — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Increase the number of DPUs to 20 and enable job bookmarking for incremental processing. — Increasing DPUs from 10 to 20 provides more memory and compute resources, directly addressing the 'java.lang.OutOfMemoryError: Java heap space' caused by insufficient memory for the single DataFrame. Enabling job bookmarking allows incremental processing, which reduces the volume of data processed per run, further mitigating memory pressure without code changes.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
This DEA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DEA-C01 exam.
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