- A
Partition by a hash of the record ID to distribute data evenly
Why wrong: Partitioning by a hash of the record ID distributes data evenly but does not support time-range pruning, leading to full partition scans for time-based queries.
- B
Do not partition; use a single prefix for all data
Why wrong: Not partitioning at all (single prefix) forces Athena to scan all objects for any query, causing high cost and latency.
- C
Partition by year, then month, then day, then hour
Partitioning by year/month/day/hour enables efficient date-range filtering and works well for both batch (daily) and real-time (hourly) use cases.
- D
Partition by source type and then by date
Why wrong: Partitioning by source type then date can lead to data skew (e.g., one source having much more data) and may not prune as effectively as a pure time-based partition for time-range queries.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 engineer is designing a data lake on Amazon S3. The data comes from various sources and must be stored in a way that supports both batch and real-time analytics. The engineer needs to partition the data to optimize query performance in Amazon Athena. Which partitioning strategy is MOST appropriate?
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
Partition by year, then month, then day, then hour
Option C is correct. Partitioning by year, month, day, and hour allows efficient query pruning for both batch (e.g., daily aggregations) and real-time (e.g., hourly) access patterns. Option A (hash of record ID) creates random partitions and is not beneficial for time-range queries. Option B (single prefix) results in full table scans and poor performance. Option D (source type then date) might cause partition skew and is less optimal for time-series analysis compared to a pure time-based hierarchy.
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.
- ✗
Partition by a hash of the record ID to distribute data evenly
Why it's wrong here
Partitioning by a hash of the record ID distributes data evenly but does not support time-range pruning, leading to full partition scans for time-based queries.
- ✗
Do not partition; use a single prefix for all data
Why it's wrong here
Not partitioning at all (single prefix) forces Athena to scan all objects for any query, causing high cost and latency.
- ✓
Partition by year, then month, then day, then hour
Why this is correct
Partitioning by year/month/day/hour enables efficient date-range filtering and works well for both batch (daily) and real-time (hourly) use cases.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Partition by source type and then by date
Why it's wrong here
Partitioning by source type then date can lead to data skew (e.g., one source having much more data) and may not prune as effectively as a pure time-based partition for time-range queries.
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.
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
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.
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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: Partition by year, then month, then day, then hour — Option C is correct. Partitioning by year, month, day, and hour allows efficient query pruning for both batch (e.g., daily aggregations) and real-time (e.g., hourly) access patterns. Option A (hash of record ID) creates random partitions and is not beneficial for time-range queries. Option B (single prefix) results in full table scans and poor performance. Option D (source type then date) might cause partition skew and is less optimal for time-series analysis compared to a pure time-based hierarchy.
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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Last reviewed: Jun 20, 2026
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