- A
Switch from a Spark ETL job to a Python shell job
Why wrong: Python shell jobs are not designed for large-scale data processing.
- B
Decrease the number of workers to reduce overhead
Why wrong: Fewer workers increase memory pressure per worker.
- C
Change the worker type from G.2X to G.1X to increase memory per worker
Why wrong: G.1X has less memory than G.2X, not more.
- D
Increase the number of workers in the job configuration
More workers distribute the data processing, reducing memory per node.
Question 983 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 uses AWS Glue ETL jobs to transform data from Amazon RDS for MySQL to Amazon S3. The transformation includes aggregations and joins. The job runs daily and processes approximately 100 GB of data. Recently, the job started failing with memory errors on the worker nodes. Which approach would MOST effectively resolve the issue without changing the logic?
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 workers in the job configuration
Increasing the number of workers distributes the memory load across more nodes, which directly addresses memory errors in a Spark ETL job without altering the transformation logic. AWS Glue Spark jobs process data in memory across workers, and insufficient total memory causes out-of-memory errors when handling 100 GB of data with aggregations and joins.
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.
- ✗
Switch from a Spark ETL job to a Python shell job
Why it's wrong here
Python shell jobs are not designed for large-scale data processing.
- ✗
Decrease the number of workers to reduce overhead
Why it's wrong here
Fewer workers increase memory pressure per worker.
- ✗
Change the worker type from G.2X to G.1X to increase memory per worker
Why it's wrong here
G.1X has less memory than G.2X, not more.
- ✓
Increase the number of workers in the job configuration
Why this is correct
More workers distribute the data processing, reducing memory per node.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might confuse worker type (memory per worker) with number of workers (total cluster memory), incorrectly assuming a larger worker type always helps, when in fact increasing the number of workers is the direct fix for memory errors in distributed Spark jobs.
Detailed technical explanation
How to think about this question
In AWS Glue Spark jobs, memory errors often stem from the shuffle phase during joins and aggregations, where data is repartitioned across workers. Increasing the number of workers (e.g., from 10 to 20) increases total cluster memory and reduces the data each worker must hold in memory, mitigating out-of-memory errors. The G.2X worker type provides 16 GB of memory and 4 vCPUs, while G.1X provides 8 GB and 2 vCPUs, so switching to G.1X would halve per-worker memory.
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
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — 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 workers in the job configuration — Increasing the number of workers distributes the memory load across more nodes, which directly addresses memory errors in a Spark ETL job without altering the transformation logic. AWS Glue Spark jobs process data in memory across workers, and insufficient total memory causes out-of-memory errors when handling 100 GB of data with aggregations and joins.
What should I do if I get this MLS-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
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