The correct step is to check the partition metadata to ensure queries are pruning partitions. When Athena scans too much data despite a partitioned dataset, the root cause is often that the query is not leveraging partition pruning—meaning it is reading all partitions instead of only the relevant ones. By running SHOW PARTITIONS or querying the information_schema, you can verify that the partition structure is correctly defined and that your WHERE clauses are filtering on partition columns. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of cost and performance optimization in Athena, a common scenario for ML data pipelines. A frequent trap is confusing data compression or file format conversion with partition pruning; while Parquet and compression reduce scan size, they do not fix queries that ignore partition boundaries. Memory tip: “Prune partitions, not just data—check your WHERE clause is cutting at the folder level.”
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. 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 analyst is using Amazon Athena to query a partitioned dataset in S3. They notice that queries are scanning more data than expected. Which step should they take during exploratory data analysis to optimize query performance?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check the partition metadata to ensure queries are pruning partitions.
Option B is correct because checking the partition metadata using SHOW PARTITIONS or querying the information_schema helps verify that the partition structure is correct and that queries are using partition pruning. Option A is incorrect because compressing data reduces storage but does not directly affect partition pruning. Option C is incorrect because converting to Parquet improves columnar scanning but does not address partition misuse. Option D is incorrect because increasing workers does not fix incorrect partition usage.
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 data to Parquet format.
Why it's wrong here
Parquet improves columnar scanning but does not fix partition issues.
✗
Use S3 Select to filter data before querying.
Why it's wrong here
S3 Select is for simple filtering, not partition optimization.
✗
Increase the number of workers in Athena.
Why it's wrong here
More workers do not reduce scanned data if partitions are not used.
✓
Check the partition metadata to ensure queries are pruning partitions.
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 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.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Check the partition metadata to ensure queries are pruning partitions. — Option B is correct because checking the partition metadata using SHOW PARTITIONS or querying the information_schema helps verify that the partition structure is correct and that queries are using partition pruning. Option A is incorrect because compressing data reduces storage but does not directly affect partition pruning. Option C is incorrect because converting to Parquet improves columnar scanning but does not address partition misuse. Option D is incorrect because increasing workers does not fix incorrect partition usage.
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.
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Question Discussion
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