- A
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Parquet with larger files improves read efficiency and reduces overhead.
- B
Copy the data to an Amazon EBS volume attached to the training instance.
Why wrong: EBS volumes are not shared and have limited throughput; not scalable.
- C
Use Amazon Athena to convert the data into a single CSV file.
Why wrong: Athena converts data but output may still have many files; also adds latency.
- D
Use S3 Select to filter data before training.
Why wrong: S3 Select reduces data transferred but does not optimize file read patterns.
Question 1,121 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 wants to use Amazon SageMaker to train a model on a dataset stored in Amazon S3. The dataset is 100 GB and consists of millions of small JSON files. What should the data engineering team do to optimize training performance?
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
✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Combining millions of small JSON files into larger Parquet files using a Spark job on Amazon EMR is correct because it reduces the overhead of S3 LIST and GET requests during training. Parquet's columnar format also improves compression and allows SageMaker to read only the necessary columns, significantly accelerating I/O-bound training workloads.
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.
- ✓
Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR.
Why this is correct
Parquet with larger files improves read efficiency and reduces overhead.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Copy the data to an Amazon EBS volume attached to the training instance.
Why it's wrong here
EBS volumes are not shared and have limited throughput; not scalable.
- ✗
Use Amazon Athena to convert the data into a single CSV file.
Why it's wrong here
Athena converts data but output may still have many files; also adds latency.
- ✗
Use S3 Select to filter data before training.
Why it's wrong here
S3 Select reduces data transferred but does not optimize file read patterns.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume S3 Select or Athena can magically optimize small-file performance, but they fail to realize that the core issue is the sheer number of S3 API requests, which only consolidation into larger files can solve.
Trap categories for this question
Command / output trap
Athena converts data but output may still have many files; also adds latency.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker's Pipe mode or File mode training reads data from S3 via the S3 API; each small JSON file requires a separate GET request, and S3 has a default request rate limit of 3,500 PUT/COPY/POST/DELETE or 5,500 GET/HEAD requests per second per prefix. With millions of files, the training job will be throttled, causing retries and slowdowns. Parquet files, combined with Spark's partitioning, reduce the number of objects to hundreds or thousands, and the columnar format enables predicate pushdown and vectorized reads, which can improve throughput by 10x or more in practice.
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 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.
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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: Combine the small JSON files into larger Parquet files using a Spark job on Amazon EMR. — Combining millions of small JSON files into larger Parquet files using a Spark job on Amazon EMR is correct because it reduces the overhead of S3 LIST and GET requests during training. Parquet's columnar format also improves compression and allows SageMaker to read only the necessary columns, significantly accelerating I/O-bound training workloads.
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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