- A
Enable pushdown predicate filtering on the job's data source.
Pushdown predicates filter data at the source, reducing data scanned.
- B
Convert Parquet files to CSV to improve read performance.
Why wrong: Parquet is columnar and optimized for analytics; CSV would degrade performance.
- C
Increase the number of DPUs for the job.
Why wrong: Adding DPUs helps but does not address the root cause of reading unnecessary partitions.
- D
Switch from Spark to Python shell for simpler processing.
Why wrong: Python shell does not support distributed processing and would be slower.
Question 1,015 of 1,786
Improving AWS Glue Job Performance with Pushdown Predicate Filtering
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 data engineering team notices that an AWS Glue ETL job, which processes hourly data from an S3 bucket, is taking progressively longer to run. The job reads Parquet files partitioned by date and hour. Which action is MOST likely to improve the job's performance?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Enable pushdown predicate filtering on the job's data source.
Option A is correct because enabling pushdown predicate filtering allows the Glue job to read only the relevant partitions (e.g., specific date and hour) instead of scanning all data. This directly addresses the symptom of progressively longer run times as data accumulates. Option B is incorrect because Parquet is a columnar format optimized for performance, while CSV would increase I/O. Option C, increasing DPUs, can improve parallelism but does not reduce the amount of data read, so it may not address the root cause. Option D, switching to Python shell, would lose Spark's distributed processing capabilities and is unlikely to improve performance.
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.
- ✓
Enable pushdown predicate filtering on the job's data source.
Why this is correct
Pushdown predicates filter data at the source, reducing data scanned.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Convert Parquet files to CSV to improve read performance.
Why it's wrong here
Parquet is columnar and optimized for analytics; CSV would degrade performance.
- ✗
Increase the number of DPUs for the job.
Why it's wrong here
Adding DPUs helps but does not address the root cause of reading unnecessary partitions.
- ✗
Switch from Spark to Python shell for simpler processing.
Why it's wrong here
Python shell does not support distributed processing and would be slower.
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.
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 |
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.
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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: Enable pushdown predicate filtering on the job's data source. — Option A is correct because enabling pushdown predicate filtering allows the Glue job to read only the relevant partitions (e.g., specific date and hour) instead of scanning all data. This directly addresses the symptom of progressively longer run times as data accumulates. Option B is incorrect because Parquet is a columnar format optimized for performance, while CSV would increase I/O. Option C, increasing DPUs, can improve parallelism but does not reduce the amount of data read, so it may not address the root cause. Option D, switching to Python shell, would lose Spark's distributed processing capabilities and is unlikely to improve performance.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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