The correct answer is to convert the CSV files to Parquet format and partition the data by date. This approach directly addresses the core issues of high data scan volume and slow performance by leveraging Parquet’s columnar storage and compression, which drastically reduces the amount of data Athena must read per query, and by partitioning on date, which allows Athena to use partition pruning to skip entire directories of older, rarely queried data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of cost-performance optimization for serverless analytics, often appearing as a trap where candidates might mistakenly look for indexing or choose only one optimization. A common memory tip is “Parquet partitions prune price” — Parquet for columnar efficiency, partitions for targeted scanning, and both together minimize cost and maximize speed.
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 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.
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 D is correct. Converting CSV to Parquet reduces scan size due to columnar storage and compression. Partitioning by date allows Athena to skip irrelevant partitions. Option A is wrong because it does not address the partitioning issue. Option B is wrong because converting to ORC alone without partitioning helps but not as much as partitioning. Option C is wrong because Athena does not support indexes.
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.
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.
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 D is correct. Converting CSV to Parquet reduces scan size due to columnar storage and compression. Partitioning by date allows Athena to skip irrelevant partitions. Option A is wrong because it does not address the partitioning issue. Option B is wrong because converting to ORC alone without partitioning helps but not as much as partitioning. Option C is wrong because Athena does not support indexes.
What should I do if I get this MLS-C01 question wrong?
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.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. Which TWO of the following are valid ways to reduce query costs in Amazon Athena? (Choose 2)
medium
A.Use UNLOAD to export query results to S3
✓ B.Partition the data in S3
C.Increase the query timeout limit
✓ D.Use columnar storage formats like Parquet
E.Enable encryption at rest on S3
Why B: Option A (partitioning) reduces data scanned, lowering cost. Option C (using columnar formats) also reduces scanned data. Option B is wrong because increasing limit doesn't reduce cost. Option D is wrong because encryption doesn't reduce cost. Option E is wrong because unload is for exporting, not cost reduction.
Variation 2. A data engineer is designing a data lake on Amazon S3 that must support both batch and streaming analytics. The data comes in Parquet format and needs to be queryable by Amazon Athena. Which partitioning strategy will optimize query performance and reduce costs?
hard
✓ A.Partition by date and hour for time-based queries
B.Store data as CSV without partitioning for simplicity
C.Partition by device_id for granular access
D.Use a single partition for all data to simplify management
Why A: Partitioning by date and hour allows Athena to prune partitions effectively for time-based queries, reducing data scanned. Option A is wrong because a single partition is not efficient. Option C is wrong because partitioning by a high-cardinality column like device_id creates many small partitions. Option D is wrong because using CSV negates the benefits of columnar storage.
Variation 3. A data engineering team is designing a data lake on Amazon S3. The data is ingested from multiple sources in JSON, CSV, and Parquet formats. The team needs to make the data available for analysis using Amazon Athena and Amazon Redshift Spectrum. The team wants to minimize data transformation costs and storage overhead. Which data storage approach should the team use?
hard
A.Load the data into Amazon Redshift cluster and then unload to S3 in Parquet
B.Store the data in its original format in S3 and use Athena to query directly
C.Store the data in its original format and use AWS Glue to convert to Parquet when queried
✓ D.Convert all data to Apache Parquet before storing in S3
Why D: Option B is correct because storing data in columnar formats like Parquet reduces storage and improves query performance. Option A is wrong because storing all data as raw JSON inflates storage. Option C is wrong because converting data to a single format increases transformation costs. Option D is wrong because using a relational database is not a data lake approach.
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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.
Question Discussion
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