The correct answer is that the first query uses a partition column, enabling partition pruning in Athena queries, while the second does not. Partition pruning works by filtering on partitioned columns like year, allowing Athena to skip scanning irrelevant data directories entirely; the first query scans only the 2023 partition, whereas the second query filters on amount, a non-partitioned column, forcing a full table scan of all partitions. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept tests your understanding of how Athena optimizes query performance through data partitioning, a common trap being that filtering on any column will speed up queries—when in fact only partition columns trigger pruning. Remember the mnemonic: “Prune the partitions, not the data rows”—if your WHERE clause doesn’t include a partition key, Athena reads everything.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
Network Topology
A data engineer is querying the AWS Glue Data Catalog table shown in the exhibit. The engineer runs an Athena query: SELECT * FROM transactions WHERE year=2023. The query returns results quickly. However, a subsequent query: SELECT * FROM transactions WHERE amount > 100 takes a long time. What is the most likely reason for the performance difference?
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.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The first query uses a partition column (year), allowing partition pruning, while the second query does not.
Option A is correct because the table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions. Option B is wrong because the data format is text (CSV), not Parquet. Option C is wrong because compression is not mentioned. Option D is wrong because the query is not partitioned correctly; the second query does not use partition columns.
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.
✗
The data is compressed, and the first query benefits from compression.
Why it's wrong here
No compression is specified.
✓
The first query uses a partition column (year), allowing partition pruning, while the second query does not.
Why this is correct
Partition pruning reduces data scanned.
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.
✗
The data is stored in Parquet format, which is optimized for columnar access.
Why it's wrong here
The table uses CSV (LazySimpleSerDe).
✗
The second query is not optimized because it uses 'SELECT *'.
Why it's wrong here
SELECT * is not the main issue; partition pruning is.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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: The first query uses a partition column (year), allowing partition pruning, while the second query does not. — Option A is correct because the table is partitioned by year and month. The first query filters on a partition column (year), so Athena prunes partitions and scans only the relevant data. The second query filters on a non-partition column (amount), so Athena scans all partitions. Option B is wrong because the data format is text (CSV), not Parquet. Option C is wrong because compression is not mentioned. Option D is wrong because the query is not partitioned correctly; the second query does not use partition columns.
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: "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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Question Discussion
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