MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist is using SageMaker to train a model. The training job is failing with a 'ResourceLimitExceeded' error. Which action should be taken to resolve this issue?
⚠ Common exam trap
A common mix-up: candidates confuse 'ResourceLimitExceeded' with an out-of-memory or insufficient capacity error, leading them to choose dataset reduction or instance type changes instead of recognizing it as a quota-based limit that requires a service limit increase.
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
✓
Request a service limit increase for the instance type.
The 'ResourceLimitExceeded' error in SageMaker indicates that the AWS account has reached the maximum number of allowed resources (e.g., instances, vCPUs, or storage) for a given instance type in the current region. Requesting a service limit increase via the AWS Service Quotas console or API directly resolves this by raising the cap for that specific instance type, allowing the training job to proceed.
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 a different AWS Region.
Why it's wrong here
Different regions may have different limits but the root cause is account limit.
- ✓
Request a service limit increase for the instance type.
Why this is correct
The error indicates the instance limit is reached; requesting an increase resolves it.
- ✗
Reduce the training dataset size.
Why it's wrong here
The error is not about data size but about instance count limit.
- ✗
Switch to a different instance type with lower resource requirements.
Why it's wrong here
This avoids the limit but does not increase capacity for the required instance type.
Visual reference
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
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
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.