MLS-C01 Modeling Practice Question
A machine learning team is using Amazon SageMaker to train a model. The training data is stored in an S3 bucket. The team wants to ensure that the training job can access the data securely without using long-lived AWS credentials. Which approach should the team use?
⚠ Common exam trap
Watch out — candidates often think embedding credentials in code (Option A) is acceptable for automation, but AWS services like SageMaker are designed to use IAM roles for temporary, scoped access, making long-lived credentials unnecessary and insecure.
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
✓
Specify an IAM role in the SageMaker training job configuration
SageMaker training jobs can assume an IAM role specified in the job configuration to obtain temporary security credentials via AWS Security Token Service (STS). This allows the training job to access the S3 bucket securely without embedding long-lived AWS access keys in code or configuration files.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Store AWS access keys in the training script
Why it's wrong here
Embedding long-lived credentials in code is insecure and against best practices.
- ✗
Use an S3 bucket policy that allows public access
Why it's wrong here
Public access would expose the data to anyone, which is insecure.
- ✓
Specify an IAM role in the SageMaker training job configuration
Why this is correct
SageMaker assumes the IAM role to access S3, providing temporary credentials and secure access.
- ✗
Create a new IAM user for each training job
Why it's wrong here
Managing many IAM users is inefficient and still involves long-lived credentials.
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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JA
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.