The answer is to add more partition columns such as hour to reduce the data scanned. This works because Athena’s partitioning feature prunes the data at the storage layer, so adding a finer-grained partition like hour allows queries filtering on a specific time range to skip entire directories of Parquet files, drastically cutting the bytes read. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to minimize Athena data scanning by adding partition columns when file sizes and compression are already optimized—a common trap is assuming that changing the file format to JSON or CSV will help, but those formats actually increase data size and scanning. The key insight is that partitioning is a physical data organization strategy, not a compression one. Memory tip: think “partition pruning” like a library—more specific shelf labels (hour) mean you grab fewer books (less data scanned) than just knowing the floor (year/month/day).
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 runs a data lake on Amazon S3 with partitions by year/month/day. A machine learning team needs to read daily data from the last 30 days for model retraining. The data format is Parquet. The team uses Amazon Athena to query the data, but the queries are slow and scanning too much data. The team has already optimized the file sizes and compression. What additional step can reduce the amount of data scanned?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Add more partition columns such as hour to reduce the scanned partitions.
Option A is correct because partitioning on additional columns like hour can further prune partitions if queries filter by time ranges. Option B is wrong because converting to JSON would increase data size. Option C is wrong because converting to CSV would also increase size. Option D is wrong because removing partitions would increase scanning.
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.
✗
Remove the partition structure and store data as single large files.
Why it's wrong here
Without partitions, queries will scan all data.
✗
Convert the Parquet files to JSON format for better query performance.
Add more partition columns such as hour to reduce the scanned partitions.
Why this is correct
More granular partitions allow queries to scan fewer files.
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 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.
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: Add more partition columns such as hour to reduce the scanned partitions. — Option A is correct because partitioning on additional columns like hour can further prune partitions if queries filter by time ranges. Option B is wrong because converting to JSON would increase data size. Option C is wrong because converting to CSV would also increase size. Option D is wrong because removing partitions would increase scanning.
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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