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?
⚠ Common exam trap
The trap is confusing SageMaker's built-in algorithms with pre-trained model hubs — candidates may pick the built-in Image Classification algorithm because it sounds like the 'official' image classification tool, but the question specifically asks for leveraging a pre-trained model, which is JumpStart's core value proposition.
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, publicly available foundation models and task-specific models (including image classification) that can be fine-tuned on your dataset, dramatically reducing training time and data requirements. For a team wanting to leverage a pre-trained model for image classification on 100,000 labeled images, JumpStart is the purpose-built feature that offers one-click deployment and fine-tuning of pre-trained models.
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 monitors tensors and gradients during training; it does not supply pre-trained weights. It is tempting because it is a training-time SageMaker feature, and would be correct when diagnosing vanishing gradients, poor convergence or anomalous tensor values in a job already running.
- ✗
SageMaker Model Monitor
Why it's wrong here
Model Monitor detects data and prediction drift on deployed endpoints; it plays no part in training. It is tempting because it is a SageMaker monitoring feature, and would be correct when production traffic must be compared against a baseline to raise quality alerts.
- ✗
SageMaker built-in Image Classification algorithm
Why it's wrong here
A built-in algorithm trains from scratch on your images; it does not load pre-trained weights, so it cannot deliver the transfer-learning speed-up the team wants. It is tempting because it handles image classification end to end, and would be correct for training a bespoke classifier on labelled data without a pre-trained backbone.
- ✓
SageMaker JumpStart
Why this is correct
SageMaker JumpStart provides pre-trained foundation and task-specific models, including image classification, that can be fine-tuned on your own dataset. This directly satisfies the requirement to start from a pre-trained model and cut training time, rather than building a model from scratch with custom training code.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.