DEA-C01 Data Operations and Support Practice Question
This DEA-C01 practice question tests your understanding of data operations and support. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Exhibit
Error log from AWS Glue job: 2024-01-01 12:00:00 ERROR: An error occurred while calling o123.pyWriteDynamicFrame. Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 123, ip-10-0-0-123.ec2.internal): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.0 GB of 10.0 GB physical memory used. Consider boosting spark.executor.memory.
A data engineer reviews the above error log from an AWS Glue ETL job. The job uses a G.1X worker type (16 GB memory). The job processes a 30 GB CSV file from S3. What should the engineer do to resolve the memory error?
Error log from AWS Glue job: 2024-01-01 12:00:00 ERROR: An error occurred while calling o123.pyWriteDynamicFrame. Error: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 1.0 failed 4 times, most recent failure: Lost task 0.3 in stage 1.0 (TID 123, ip-10-0-0-123.ec2.internal): ExecutorLostFailure (executor 2 exited caused by one of the running tasks) Reason: Container killed by YARN for exceeding memory limits. 10.0 GB of 10.0 GB physical memory used. Consider boosting spark.executor.memory.
A
Convert the input file from CSV to Parquet format.
Why wrong: While Parquet is more efficient, the executor memory limit is still 10 GB and may still be exceeded.
B
Set 'spark.executor.memory' to 12g in the job parameters.
Increasing executor memory to 12 GB gives the task more headroom within the 16 GB container.
C
Decrease the number of workers to 1 to reduce memory overhead.
Why wrong: Fewer workers reduce parallelism and total memory, but the executor memory limit remains.
D
Increase the number of workers from 2 to 4.
Why wrong: More workers do not increase memory per executor; each executor still has the same limit.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set 'spark.executor.memory' to 12g in the job parameters.
Option B is correct because the error indicates that the executor ran out of memory (10 GB used of 10 GB limit). Increasing the Spark executor memory to 12 GB (since G.1X has 16 GB total, leaving room for overhead) will prevent the container from being killed. Option A is wrong because increasing the number of workers does not increase per-executor memory. Option C is wrong because reducing the number of workers reduces total memory but does not fix per-executor limits. Option D is wrong because converting to Parquet reduces file size but does not change the memory limit per executor.
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.
✗
Convert the input file from CSV to Parquet format.
Why it's wrong here
While Parquet is more efficient, the executor memory limit is still 10 GB and may still be exceeded.
✓
Set 'spark.executor.memory' to 12g in the job parameters.
Why this is correct
Increasing executor memory to 12 GB gives the task more headroom within the 16 GB container.
Related concept
Read the scenario before looking for a memorised answer.
✗
Decrease the number of workers to 1 to reduce memory overhead.
Why it's wrong here
Fewer workers reduce parallelism and total memory, but the executor memory limit remains.
✗
Increase the number of workers from 2 to 4.
Why it's wrong here
More workers do not increase memory per executor; each executor still has the same limit.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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.
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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: Set 'spark.executor.memory' to 12g in the job parameters. — Option B is correct because the error indicates that the executor ran out of memory (10 GB used of 10 GB limit). Increasing the Spark executor memory to 12 GB (since G.1X has 16 GB total, leaving room for overhead) will prevent the container from being killed. Option A is wrong because increasing the number of workers does not increase per-executor memory. Option C is wrong because reducing the number of workers reduces total memory but does not fix per-executor limits. Option D is wrong because converting to Parquet reduces file size but does not change the memory limit per executor.
What should I do if I get this DEA-C01 question wrong?
Identify which DEA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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