- A
Migrate the ETL jobs to Amazon EMR with Apache Spark
Why wrong: Migrating to Amazon EMR with Apache Spark is an alternative but introduces additional complexity and cost. It is not the most straightforward solution for simply increasing memory on existing AWS Glue jobs.
- B
Use AWS Glue Flex execution to allocate resources dynamically
Why wrong: AWS Glue Flex execution is a pricing model that uses spare capacity, which can be preempted and does not dynamically allocate resources for variable memory needs. It is not designed to solve insufficient memory issues reliably.
- C
Increase the number of DPUs (Data Processing Units) for all jobs
Increasing the number of DPUs adds more memory and compute capacity, directly addressing insufficient memory. This approach can be scaled up during spikes to handle variable workloads efficiently.
- D
Split the jobs into smaller steps and run them sequentially
Why wrong: Splitting jobs into smaller steps and running them sequentially may reduce memory usage per job but increases runtime and complexity. It does not efficiently handle spikes as it does not add resources.
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 is using AWS Glue to run ETL jobs that transform data from Amazon S3 to Amazon Redshift. The jobs are currently failing due to insufficient memory. The data volume varies, with occasional spikes. Which solution should be used to handle the variable memory requirements efficiently?
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 (Data Processing Units) for all jobs
To handle variable memory requirements efficiently, increasing the number of DPUs for all jobs ensures sufficient memory during spikes. AWS Glue Flex execution uses spare capacity and can be preempted, so it does not guarantee resource availability and is not suitable for handling memory insufficiencies. Option C provides a direct way to allocate more resources, preventing job failures due to insufficient memory.
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.
- ✗
Migrate the ETL jobs to Amazon EMR with Apache Spark
Why it's wrong here
Migrating to Amazon EMR with Apache Spark is an alternative but introduces additional complexity and cost. It is not the most straightforward solution for simply increasing memory on existing AWS Glue jobs.
- ✗
Use AWS Glue Flex execution to allocate resources dynamically
Why it's wrong here
AWS Glue Flex execution is a pricing model that uses spare capacity, which can be preempted and does not dynamically allocate resources for variable memory needs. It is not designed to solve insufficient memory issues reliably.
- ✓
Increase the number of DPUs (Data Processing Units) for all jobs
Why this is correct
Increasing the number of DPUs adds more memory and compute capacity, directly addressing insufficient memory. This approach can be scaled up during spikes to handle variable workloads efficiently.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Split the jobs into smaller steps and run them sequentially
Why it's wrong here
Splitting jobs into smaller steps and running them sequentially may reduce memory usage per job but increases runtime and complexity. It does not efficiently handle spikes as it does not add resources.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse increasing DPUs (Option C) as the only way to handle memory issues, overlooking AWS Glue Flex execution's dynamic allocation feature that efficiently manages variable workloads without over-provisioning.
Detailed technical explanation
How to think about this question
AWS Glue Flex execution leverages spare EC2 capacity to run jobs at a lower cost, with dynamic resource allocation that scales DPUs up to 100 per job based on workload. Under the hood, it uses a preemptible-like model where jobs can be interrupted if capacity is reclaimed, but for variable memory spikes, it allows Glue to automatically adjust the number of executors and memory per executor via Apache Spark's dynamic allocation (spark.dynamicAllocation.enabled). In a real-world scenario, a company processing daily logs with occasional Black Friday spikes would benefit from Flex execution to avoid provisioning for peak load constantly.
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.
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 DPUs (Data Processing Units) for all jobs — To handle variable memory requirements efficiently, increasing the number of DPUs for all jobs ensures sufficient memory during spikes. AWS Glue Flex execution uses spare capacity and can be preempted, so it does not guarantee resource availability and is not suitable for handling memory insufficiencies. Option C provides a direct way to allocate more resources, preventing job failures due to insufficient memory.
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.
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Last reviewed: Jul 4, 2026
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