- A
Use Amazon Athena to query the data and output results to a new S3 location
Why wrong: Athena is for querying, not for preparing training data efficiently.
- B
Enable S3 Transfer Acceleration on the bucket
Why wrong: Transfer Acceleration improves speed but does not reduce the number of requests.
- C
Combine the small files into larger files (e.g., 100 MB) using a preprocessing step
Larger files reduce the number of GET requests, mitigating throttling.
- D
Increase the number of SageMaker training instances to distribute the load
Why wrong: More instances increase concurrent requests, potentially worsening throttling.
Question 686 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 is running a machine learning training job on Amazon SageMaker that reads training data from an S3 bucket. The job fails intermittently with an S3 throttling error. The data is partitioned across thousands of small files (average 100 KB). Which strategy is MOST effective to resolve the throttling issue?
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 files into larger files (e.g., 100 MB) using a preprocessing step
Option C is correct because S3 throttling errors (HTTP 503) occur when many small files cause a high request rate per prefix. By combining thousands of 100 KB files into fewer 100 MB files, you drastically reduce the number of GET requests, staying within S3's 5,500 GET requests per second per prefix limit. This preprocessing step directly addresses the root cause of the throttling without changing the training infrastructure.
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.
- ✗
Use Amazon Athena to query the data and output results to a new S3 location
Why it's wrong here
Athena is for querying, not for preparing training data efficiently.
- ✗
Enable S3 Transfer Acceleration on the bucket
Why it's wrong here
Transfer Acceleration improves speed but does not reduce the number of requests.
- ✓
Combine the small files into larger files (e.g., 100 MB) using a preprocessing step
Why this is correct
Larger files reduce the number of GET requests, mitigating throttling.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of SageMaker training instances to distribute the load
Why it's wrong here
More instances increase concurrent requests, potentially worsening throttling.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse network-level optimizations (Transfer Acceleration) or parallelization (more instances) with the fundamental S3 request rate limit, which is a per-prefix throughput constraint, not a bandwidth issue.
Detailed technical explanation
How to think about this question
S3 throttling is per prefix, meaning all objects under a given prefix share a capacity of 5,500 GET/HEAD requests per second. With thousands of 100 KB files, even a single training instance can easily exceed this limit during data loading. Combining files into larger objects (e.g., 100 MB) reduces the object count, and using SageMaker's Pipe input mode or ShardedByS3Key can further distribute reads across prefixes. In practice, frameworks like TensorFlow or PyTorch often use sharded TFRecord or Parquet files to avoid this exact bottleneck.
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.
Visual reference
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 files into larger files (e.g., 100 MB) using a preprocessing step — Option C is correct because S3 throttling errors (HTTP 503) occur when many small files cause a high request rate per prefix. By combining thousands of 100 KB files into fewer 100 MB files, you drastically reduce the number of GET requests, staying within S3's 5,500 GET requests per second per prefix limit. This preprocessing step directly addresses the root cause of the throttling without changing the training infrastructure.
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
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.
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