- A
Place all training instances in a Cluster Placement Group
Why wrong: Placement groups reduce network latency between instances but do not affect S3 access latency.
- B
Enable S3 Transfer Acceleration on the bucket
Why wrong: S3 Transfer Acceleration is for fast uploads over long distances, not for improving GET latency for training.
- C
Mount an Amazon FSx for Lustre file system integrated with the S3 bucket
FSx for Lustre provides a high-performance file system that can read data from S3 with low latency.
- D
Use Elastic Fabric Adapter (EFA) for training instances
Why wrong: EFA improves inter-instance communication for distributed training, not data access from S3.
Question 1,508 of 1,755
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 science team uses Amazon SageMaker to train models on a dataset stored in Amazon S3. The dataset is 2 TB and is accessed by multiple training jobs. The team notices that training jobs are slow due to high S3 GET request latency. Which solution would provide the fastest and most cost-effective data access?
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
✓
Mount an Amazon FSx for Lustre file system integrated with the S3 bucket
Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked to an S3 bucket, allowing training instances to access data with sub-millisecond latency instead of S3 GET request latency. This integration enables data to be read from the Lustre file system at up to hundreds of gigabytes per second of throughput, which is significantly faster than reading directly from S3, and it is cost-effective because you only pay for the storage and throughput you provision during training.
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.
- ✗
Place all training instances in a Cluster Placement Group
Why it's wrong here
Placement groups reduce network latency between instances but do not affect S3 access latency.
- ✗
Enable S3 Transfer Acceleration on the bucket
Why it's wrong here
S3 Transfer Acceleration is for fast uploads over long distances, not for improving GET latency for training.
- ✓
Mount an Amazon FSx for Lustre file system integrated with the S3 bucket
Why this is correct
FSx for Lustre provides a high-performance file system that can read data from S3 with low latency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Elastic Fabric Adapter (EFA) for training instances
Why it's wrong here
EFA improves inter-instance communication for distributed training, not data access from S3.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse network-level optimizations (Placement Groups, EFA) or upload acceleration (S3 Transfer Acceleration) with the actual data access bottleneck, which is the latency of S3 GET requests when reading a large dataset repeatedly during training.
Detailed technical explanation
How to think about this question
FSx for Lustre uses a distributed, parallel file system architecture that stripes data across multiple network-attached storage servers, enabling aggregate throughput that scales linearly with the number of file servers. When linked to an S3 bucket, it lazily loads data on first access (lazy loading) or can pre-load data using a data repository task, and it supports transparent access to S3 objects as files. In real-world scenarios, teams often use FSx for Lustre with SageMaker by creating a file system in the same VPC and subnet as the training instances, then mounting it via the SageMaker `TrainingInput` with `FileSystemType='FSxLustre'` to achieve 10-100x faster data loading compared to direct S3 access.
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: Mount an Amazon FSx for Lustre file system integrated with the S3 bucket — Amazon FSx for Lustre provides a high-performance, POSIX-compliant file system that can be directly linked to an S3 bucket, allowing training instances to access data with sub-millisecond latency instead of S3 GET request latency. This integration enables data to be read from the Lustre file system at up to hundreds of gigabytes per second of throughput, which is significantly faster than reading directly from S3, and it is cost-effective because you only pay for the storage and throughput you provision during training.
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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