- A
Ensure queries filter on partition columns (year, month, day, hour).
Partition pruning reduces scanned data.
- B
Increase the number of partitions by adding a partition for minute.
Why wrong: More partitions increase metadata overhead; not beneficial.
- C
Convert data from CSV to Parquet format.
Parquet is columnar and reduces scanned data.
- D
Use CSV format with GZIP compression.
Why wrong: CSV is not columnar; still scans entire rows.
- E
Use S3 storage classes like S3 Intelligent-Tiering for cost savings.
Intelligent-Tiering can reduce storage costs for data lake.
Question 308 of 1,755
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 choices
Why each option matters
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).
Option A is correct because Athena charges based on the amount of data scanned per query. By filtering on partition columns (year, month, day, hour), Athena uses partition pruning to skip reading irrelevant S3 prefixes, drastically reducing the data scanned and thus lowering both cost and query latency.
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
The trap here is that candidates often think more granular partitions (e.g., minute) always improve performance, but in Athena, excessive partitions increase metadata overhead and can slow down queries due to the overhead of listing many small S3 prefixes.
Detailed technical explanation
How to think about this question
Parquet stores data in a columnar layout with embedded statistics (min/max, null counts) per row group, enabling Athena to skip entire row groups when filters are applied. Partition pruning works by leveraging the Hive-style partition layout (e.g., s3://bucket/year=2025/month=03/day=15/hour=10/) so that Athena only lists and reads the relevant prefixes, which is critical for cost control in pay-per-query models.
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.
TExam Day Tips
- 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.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
What to study next
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
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). — Option A is correct because Athena charges based on the amount of data scanned per query. By filtering on partition columns (year, month, day, hour), Athena uses partition pruning to skip reading irrelevant S3 prefixes, drastically reducing the data scanned and thus lowering both cost and query latency.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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