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MLS-C01 Data Engineering Practice Question

This MLS-C01 practice question tests your understanding of data engineering. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 responsible for managing a data lake on Amazon S3. The data lake contains CSV files from various sources, totaling 10 TB. The engineer needs to make this data queryable using Amazon Athena. However, Athena queries are currently taking a long time and scanning large amounts of data. The engineer has noticed that the CSV files are not partitioned, and there are no indexes. The engineer wants to improve query performance and reduce costs. The data is accessed frequently for the last 30 days, but older data is rarely queried. The engineer also wants to minimize the amount of data scanned by Athena. What should the engineer 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
Convert the CSV files to JSON format and use Athena to query them.
Why wrong: JSON is also text-based; it does not reduce scan size as effectively as columnar formats.
B
Convert the CSV files to Parquet format and partition the data by date.
Parquet is columnar and compressed; partitioning by date allows partition pruning, reducing scan size.
C
Create indexes on the S3 objects using AWS Glue.
Why wrong: Athena does not use indexes; it scans data directly.
D
Convert the CSV files to ORC format and create a view in Athena.
Why wrong: ORC is a columnar format, but without partitioning, queries still scan all data.

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

✓

Convert the CSV files to Parquet format and partition the data by date.

Option B is the best choice. Converting CSV to Parquet reduces data scanned due to columnar storage and compression. Partitioning by date allows Athena to skip older data that is rarely queried, further minimizing scan size and cost. Option A (JSON) does not improve performance significantly and still lacks partitioning. Option C is invalid because Athena does not support indexes. Option D (ORC) is columnar but without partitioning it performs worse than Parquet with partitioning, and views do not reduce scan size.

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.

✗
Convert the CSV files to JSON format and use Athena to query them.
Why it's wrong here
JSON is also text-based; it does not reduce scan size as effectively as columnar formats.
✓
Convert the CSV files to Parquet format and partition the data by date.
Why this is correct
Parquet is columnar and compressed; partitioning by date allows partition pruning, reducing scan size.
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.
✗
Create indexes on the S3 objects using AWS Glue.
Why it's wrong here
Athena does not use indexes; it scans data directly.
✗
Convert the CSV files to ORC format and create a view in Athena.
Why it's wrong here
ORC is a columnar format, but without partitioning, queries still scan all data.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.

Detailed technical explanation

How to think about this question

This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.

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.
Use explanations to understand the rule behind the answer.

TExam Day Tips

Underline the problem statement mentally.
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 MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.

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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: Convert the CSV files to Parquet format and partition the data by date. — Option B is the best choice. Converting CSV to Parquet reduces data scanned due to columnar storage and compression. Partitioning by date allows Athena to skip older data that is rarely queried, further minimizing scan size and cost. Option A (JSON) does not improve performance significantly and still lacks partitioning. Option C is invalid because Athena does not support indexes. Option D (ORC) is columnar but without partitioning it performs worse than Parquet with partitioning, and views do not reduce scan size.

What should I do if I get this MLS-C01 question wrong?

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.