MLA-C01 ML Model Development Practice Question
A company is using SageMaker to train a model for image classification. The training dataset contains 100,000 labeled images. The team wants to use a pre-trained model to reduce training time. Which SageMaker feature should they use?
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
✓
SageMaker JumpStart
SageMaker JumpStart provides pre-trained models that can be fine-tuned on custom datasets, reducing training time and data requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Debugger
Why it's wrong here
Debugger is for monitoring, not for providing pre-trained models.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor is for production inference monitoring.
- ✗
SageMaker built-in Image Classification algorithm
Why it's wrong here
The built-in algorithm can be trained from scratch or fine-tuned, but JumpStart offers more pre-trained models.
- ✓
SageMaker JumpStart
Why this is correct
JumpStart offers pre-trained models for transfer learning.
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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.