MLS-C01 Data Engineering Practice Question
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?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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.
- ✗
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.
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 |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.