MLS-C01 Modeling Practice Question
A data science team is using Amazon SageMaker to train a deep learning model for object detection using the built-in SSD algorithm. The dataset consists of 100,000 labeled images stored in a SageMaker Pipe Mode input. The training job uses a single ml.p3.2xlarge instance. After 2 hours, the training job fails with the error 'ResourceLimitExceeded: The account-level service limit for ml.p3.2xlarge for training job usage is 1. Contact AWS Support to request a limit increase'. However, the team has already submitted a limit increase request and it was approved for 5 instances. What is the most likely cause of the error?
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
✓
The service limit increase has not yet been applied to the account in the current region
The error 'ResourceLimitExceeded' indicates that the account's service limit for ml.p3.2xlarge training instances has been exceeded. Even though the team requested and received approval for a limit increase to 5 instances, the increase may not have taken effect yet in the current region. AWS service limit increases are applied per region, and there can be a propagation delay after approval. Option A (GPU memory) would cause a different error such as 'OutOfMemory'. Option B (algorithm requirement) is unrelated because the SSD algorithm does run on the chosen instance. Option D (S3 permissions) would result in an 'AccessDenied' error, not a limit error. Therefore, option C is the correct answer.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The instance is running out of GPU memory
Why it's wrong here
A GPU memory error would manifest as 'OutOfMemory', not 'ResourceLimitExceeded'.
- ✗
The built-in SSD algorithm requires a GPU instance type with at least 16 GB of GPU memory
Why it's wrong here
The built-in SSD algorithm can run on the ml.p3.2xlarge instance; this is not the cause of the limit error.
- ✓
The service limit increase has not yet been applied to the account in the current region
Why this is correct
The limit increase may not have been applied yet in the region, causing the 'ResourceLimitExceeded' error even though the increase was approved.
- ✗
The IAM role does not have permission to access the S3 bucket for model artifacts
Why it's wrong here
An IAM permissions issue would result in an 'AccessDenied' error, not a limit error.
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
Courseiva writes every MLS-C01 question from scratch — 1,672 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or 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.