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MLA-C01 Practice Question: A data scientist is using Amazon SageMaker Studio…
A data scientist is using Amazon SageMaker Studio to develop a model. The training job is taking longer than expected. The data scientist suspects that the data is being downloaded from Amazon S3 each time the training starts. What is the BEST way to reduce data loading time?
⚠ Common exam trap
It's easy for candidates to choose a 'bigger instance' (Option C) as a brute-force fix, overlooking that Pipe mode fundamentally changes the data access pattern to eliminate the download bottleneck entirely.
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
✓
Use SageMaker Pipe Input mode to stream data directly from S3.
SageMaker Pipe input mode streams data directly from S3 into the training algorithm without first downloading it to the training instance's local storage. This eliminates the bottleneck of copying entire datasets, reducing startup time and disk usage. It is the most direct and efficient way to address the issue of repeated downloads from S3.
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 SageMaker Pipe Input mode to stream data directly from S3.
Why this is correct
Pipe mode streams data without downloading, reducing start time.
- ✗
Enable S3 transfer acceleration and cache the data in S3.
Why it's wrong here
Acceleration speeds up upload, not download, and no caching.
- ✗
Use a larger instance type with more network bandwidth.
Why it's wrong here
Increases throughput but still downloads the full dataset each time.
- ✗
Use Amazon FSx for Lustre to mount a high-performance file system.
Why it's wrong here
Amazon FSx for Lustre provides a high-performance file system, but it does not directly address the repeated downloading of data from Amazon S3 for each training run. The issue stems from how SageMaker is configured to retrieve data from S3, not the performance of the target file system itself. This option is tempting because it offers high-throughput storage, which is crucial for data loading. It would be the correct choice if the bottleneck was accessing data *already present* on a shared, POSIX-compliant file system, or if the strategy involved pre-loading data once to FSx for Lustre for subsequent training jobs to access directly.
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 |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-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 MLA-C01 exam.