MLS-C01 Data Engineering Practice Question
A machine learning team is using Amazon SageMaker to train a model on a dataset stored in S3. The training job reads data from S3 using Pipe input mode, but the training is slow. The team wants to improve data throughput. Which THREE actions should they take?
⚠ Common exam trap
Test-takers frequently assume Pipe mode is always faster because it avoids disk writes, but they overlook that File mode with a larger instance can achieve higher throughput by leveraging parallel downloads and local caching, especially when the dataset is large or the algorithm benefits from random 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 the S3 bucket using an S3 file system and use File mode with a larger instance type.
Mounting an S3 bucket using an S3 file system (e.g., via mount-s3 or s3fs) and switching to File mode allows the training instance to access data as local files, eliminating the overhead of streaming decompression and per-record parsing inherent in Pipe mode. Using a larger instance type provides more network bandwidth and CPU resources to handle the file I/O, directly improving data throughput for large datasets.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable S3 Transfer Acceleration on the bucket.
Why it's wrong here
Transfer Acceleration improves long-distance uploads, not training data reads.
- ✓
Mount the S3 bucket using an S3 file system and use File mode with a larger instance type.
Why this is correct
File mode with high-bandwidth instances can improve throughput.
- ✓
Use Amazon S3 VPC Gateway Endpoint to reduce data transfer costs and improve latency.
Why this is correct
VPC endpoints improve network performance.
- ✓
Use Amazon EFS as the data source for training.
Why this is correct
EFS provides high throughput for sequential reads.
- ✗
Use Amazon ElastiCache to cache the training data.
Why it's wrong here
ElastiCache is not designed for large-scale training data.
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
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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.