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Data Engineering →hardMultiple ChoiceObjective-mapped

MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. 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.

A company uses AWS Glue ETL jobs to transform CSV data from an S3 bucket into Parquet. The jobs often fail with memory errors when processing large datasets. They want to minimize cost and improve reliability. What should they do?

Clue words in this question

Noticing these words before you look at the options changes how you read each choice.

Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

A
Use G.1X or G.2X worker types and increase the number of DPUs per worker.
G.1X workers provide more memory and vCPU per worker, reducing OOM errors for memory-intensive transformations.
B
Use Amazon Athena with CTAS queries to convert the data to Parquet.
Why wrong: Athena is interactive and incurs per-query costs; CTAS is good for one-time conversion but not for scheduled ETL workflows.
C
Switch to S3 Batch Operations with AWS Lambda to process the files individually.
Why wrong: S3 Batch Operations is for simple object-level actions, not complex ETL like CSV-to-Parquet conversion.
D
Increase the number of workers in the Glue job configuration.
Why wrong: Adding workers increases parallelism but does not increase memory per worker; memory errors persist if each task requires more memory.

Full breakdown with real-world context →

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

✓

Use G.1X or G.2X worker types and increase the number of DPUs per worker.

The G.1X and G.2X worker types provide more memory per worker (16 GB and 32 GB, respectively) compared to the standard G.0X worker (4 GB). By using these worker types and increasing the number of DPUs per worker, you allocate more memory to each task, reducing out-of-memory errors when processing large datasets. This approach also optimizes cost by using fewer, more powerful workers instead of many underpowered ones, improving reliability without unnecessary scaling.

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.

✓
Use G.1X or G.2X worker types and increase the number of DPUs per worker.
Why this is correct
G.1X workers provide more memory and vCPU per worker, reducing OOM errors for memory-intensive transformations.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Athena with CTAS queries to convert the data to Parquet.
Why it's wrong here
Athena is interactive and incurs per-query costs; CTAS is good for one-time conversion but not for scheduled ETL workflows.
✗
Switch to S3 Batch Operations with AWS Lambda to process the files individually.
Why it's wrong here
S3 Batch Operations is for simple object-level actions, not complex ETL like CSV-to-Parquet conversion.
✗
Increase the number of workers in the Glue job configuration.
Why it's wrong here
Adding workers increases parallelism but does not increase memory per worker; memory errors persist if each task requires more memory.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume increasing the number of workers (Option D) is the universal fix for performance issues, but in Glue, memory errors are typically caused by insufficient per-worker memory, not a lack of parallelism.

Detailed technical explanation

How to think about this question

Under the hood, AWS Glue uses Apache Spark, where each worker runs an executor with a fixed memory heap. The G.1X and G.2X worker types increase the executor memory (spark.executor.memory) and the number of cores, allowing larger data partitions to be processed in memory without spilling to disk. In practice, for datasets with wide rows or complex transformations, using G.2X workers can reduce shuffle spills and task retries, directly improving job stability and runtime.

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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.

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 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: Use G.1X or G.2X worker types and increase the number of DPUs per worker. — The G.1X and G.2X worker types provide more memory per worker (16 GB and 32 GB, respectively) compared to the standard G.0X worker (4 GB). By using these worker types and increasing the number of DPUs per worker, you allocate more memory to each task, reducing out-of-memory errors when processing large datasets. This approach also optimizes cost by using fewer, more powerful workers instead of many underpowered ones, improving reliability without unnecessary scaling.

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.

Are there clue words in this question I should notice?

Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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This MLS-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 MLS-C01 exam.