- A
Filter unnecessary records early in the transformation
Reducing data volume early decreases memory usage.
- B
Increase the number of DPUs for the Glue job
More DPUs provide more memory and parallelism.
- C
Partition the input data on the join keys
Partitioning can reduce shuffle and memory pressure.
- D
Switch from Spark to Python shell
Why wrong: Python shell has limited memory and is not suitable for large joins.
- E
Use a smaller worker type
Why wrong: Smaller workers reduce memory, worsening the problem.
Question 645 of 1,000
hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data engineer is using AWS Glue to run an ETL…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 engineer is using AWS Glue to run an ETL job that joins two large datasets and writes the output to S3 for ML training. The job is failing due to out-of-memory errors. Which THREE actions can help resolve this issue? (Select THREE.)
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
✓
Filter unnecessary records early in the transformation
Option A is correct because filtering unnecessary records early in the transformation reduces the amount of data that needs to be processed and shuffled, which directly lowers memory pressure. In AWS Glue, applying filters before joins or aggregations minimizes the dataset size in the Spark execution plan, helping to avoid out-of-memory errors.
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.
- ✓
Filter unnecessary records early in the transformation
Why this is correct
Reducing data volume early decreases memory usage.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Increase the number of DPUs for the Glue job
Why this is correct
More DPUs provide more memory and parallelism.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Partition the input data on the join keys
Why this is correct
Partitioning can reduce shuffle and memory pressure.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch from Spark to Python shell
Why it's wrong here
Python shell has limited memory and is not suitable for large joins.
- ✗
Use a smaller worker type
Why it's wrong here
Smaller workers reduce memory, worsening the problem.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might think reducing worker size (Option E) saves costs and helps memory, but it actually reduces available memory per worker, making out-of-memory errors more likely.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue uses Apache Spark for distributed processing, and out-of-memory errors often occur during the shuffle phase when data is repartitioned for joins or aggregations. Filtering early reduces the shuffle size, while increasing DPUs adds more executors to distribute the memory load. Partitioning input data on join keys enables Spark to perform a co-partitioned join, avoiding expensive shuffles and reducing memory usage per task.
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.
Visual reference
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Filter unnecessary records early in the transformation — Option A is correct because filtering unnecessary records early in the transformation reduces the amount of data that needs to be processed and shuffled, which directly lowers memory pressure. In AWS Glue, applying filters before joins or aggregations minimizes the dataset size in the Spark execution plan, helping to avoid out-of-memory errors.
What should I do if I get this MLA-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.
About these practice questions
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Last reviewed: Jul 4, 2026
This MLA-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 MLA-C01 exam.
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