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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 Amazon EMR to run Spark jobs on a transient cluster that processes data from S3. The jobs are failing with 'OutOfMemory' errors. The data engineer has already increased the executor memory. Which additional configuration change would MOST likely resolve the issue?

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.

A
Use fewer, larger instance types for the core nodes
Why wrong: Fewer nodes could concentrate data and worsen memory issues.
B
Increase the number of partitions in the data
More partitions means smaller data per task, reducing memory usage.
C
Increase the driver memory
Why wrong: OutOfMemory usually occurs in executors, not driver.
D
Increase the number of executors
Why wrong: More executors can help but may not reduce data per executor if parallelism is already high.

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

✓

Increase the number of partitions in the data

The 'OutOfMemory' errors in Spark on EMR typically occur when individual partitions hold too much data for the executor's memory to process. Increasing the number of partitions distributes the data more evenly across available memory, reducing the per-partition size and preventing memory overflow during shuffle or aggregation operations. This directly addresses the root cause of memory pressure, whereas simply increasing executor memory may only delay the failure.

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 fewer, larger instance types for the core nodes
Why it's wrong here
Fewer nodes could concentrate data and worsen memory issues.
✓
Increase the number of partitions in the data
Why this is correct
More partitions means smaller data per task, reducing memory usage.
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.
✗
Increase the driver memory
Why it's wrong here
OutOfMemory usually occurs in executors, not driver.
✗
Increase the number of executors
Why it's wrong here
More executors can help but may not reduce data per executor if parallelism is already high.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often assume adding more memory (executor or driver) or scaling vertically (larger instances) is the solution, but the exam tests understanding that memory errors in Spark are frequently caused by partition size imbalance, not insufficient total memory.

Detailed technical explanation

How to think about this question

In Spark, the default parallelism is often tied to the number of input files or blocks (e.g., 128 MB per partition from S3). When data is skewed or transformations like groupBy or join are applied, partitions can balloon in size. By explicitly repartitioning (e.g., df.repartition(n) or setting spark.sql.shuffle.partitions), you control the number of output partitions, ensuring each executor handles a manageable chunk. A real-world scenario is a 1 TB dataset with 100 default partitions (10 GB each) causing OOM; increasing to 1000 partitions reduces each to ~1 GB, fitting within executor 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

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: Increase the number of partitions in the data — The 'OutOfMemory' errors in Spark on EMR typically occur when individual partitions hold too much data for the executor's memory to process. Increasing the number of partitions distributes the data more evenly across available memory, reducing the per-partition size and preventing memory overflow during shuffle or aggregation operations. This directly addresses the root cause of memory pressure, whereas simply increasing executor memory may only delay the failure.

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: "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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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.