The correct actions are converting to columnar formats like Parquet, using partition pruning with WHERE clauses, and compressing data to reduce scan volume. These three techniques directly improve Amazon Athena query performance by minimizing the amount of data read from S3, which lowers both latency and cost. Parquet’s columnar storage allows Athena to skip irrelevant columns, while partition pruning on year, month, day, and hour columns limits scans to only the necessary partitions, and compression reduces storage footprint and I/O. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of data optimization for analytics pipelines, a common scenario when working with large-scale ML training datasets in S3. A frequent trap is assuming more partitions always help—in reality, excessive partitions increase metadata overhead and slow queries. Remember the mnemonic “PCC” for Partition, Columnar, Compress to lock in the three pillars of Athena cost and performance optimization.
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 company uses Amazon Athena to query a data lake in Amazon S3. The data is partitioned by year, month, day, and hour. The team notices that queries are slow and expensive. The team wants to improve performance and reduce costs. Which THREE actions should the team take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Ensure queries filter on partition columns (year, month, day, hour).
Options A, D, and E are correct. Converting to columnar formats like Parquet reduces the amount of data scanned. Partition pruning using WHERE clauses on partition columns reduces scanned partitions. Compressing data reduces storage and scan volume. Option B is wrong because increasing partitions beyond need can increase overhead. Option C is wrong because using CSV is less efficient.
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.
✓
Ensure queries filter on partition columns (year, month, day, hour).
Why this is correct
Partition pruning reduces scanned data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the number of partitions by adding a partition for minute.
Why it's wrong here
More partitions increase metadata overhead; not beneficial.
✓
Convert data from CSV to Parquet format.
Why this is correct
Parquet is columnar and reduces scanned data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use CSV format with GZIP compression.
Why it's wrong here
CSV is not columnar; still scans entire rows.
✓
Use S3 storage classes like S3 Intelligent-Tiering for cost savings.
Why this is correct
Intelligent-Tiering can reduce storage costs for data lake.
Related concept
Read the scenario before looking for a memorised answer.
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: Ensure queries filter on partition columns (year, month, day, hour). — Options A, D, and E are correct. Converting to columnar formats like Parquet reduces the amount of data scanned. Partition pruning using WHERE clauses on partition columns reduces scanned partitions. Compressing data reduces storage and scan volume. Option B is wrong because increasing partitions beyond need can increase overhead. Option C is wrong because using CSV is less efficient.
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.
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. A data engineer is investigating why an Athena query against the my-data-lake bucket is slow. The query filters on year, month, and day. The exhibit shows the metadata of one Parquet file. What is the MOST likely cause of the slow query?
hard
A.The version ID is null, causing data inconsistency
✓ B.The file is too large, causing Athena to process it in a single task
C.The partition columns are not being used in the query
D.The storage class is STANDARD, which is slower than GLACIER
Why B: The file is 1 GB (1073741824 bytes), which is large for a single Parquet file. Athena splits files into tasks; a single large file cannot be parallelized, causing slow performance. Partitioning is fine, but file size matters. The metadata is not missing. Storage class is standard. Versioning is not enabled.
Variation 2. A company stores IoT sensor data in Amazon S3 and uses Amazon Athena for ad-hoc queries. The data is partitioned by date, but queries are still slow and expensive. Which TWO actions can improve query performance and reduce cost? (Choose TWO.)
easy
✓ A.Use S3 lifecycle policies to compact small files into larger ones
✓ B.Convert the data from CSV to Parquet format
C.Disable server-side encryption on the S3 bucket
D.Use AWS Glue instead of Athena for querying
E.Increase the number of partitions to hour-level granularity
Why A: Option A (convert to Parquet) reduces data scanned. Option C (compact small files) reduces overhead. Option B (increase partitions) can create many small files. Option D (use Glue instead) changes service. Option E (disable encryption) is not related to performance.
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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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