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MLA-C01 Practice Question: A data scientist needs to annotate a large…
A data scientist needs to annotate a large dataset of images for an object detection model. The team wants to minimize manual labeling effort and cost. Which Amazon 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 Ground Truth
SageMaker Ground Truth provides labeling workflows with built-in active learning, which automatically selects the most informative images for human review and uses machine learning to label the rest, reducing manual effort and cost.
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 Ground Truth
Why this is correct
SageMaker Ground Truth provides managed labelling workflows with automatic data labelling and human review, reducing manual annotation effort and cost. This directly satisfies the requirement to annotate a large image dataset efficiently for object detection.
- ✗
SageMaker Feature Store
Why it's wrong here
Feature Store centralises engineered feature storage and online serving for training and inference; it performs no image annotation and cannot reduce labelling effort. It is tempting because it supports ML workflows, and would be correct when teams need to reuse and serve consistent features across models.
- ✗
SageMaker Studio Classic
Why it's wrong here
Studio Classic is an integrated development environment for notebooks, training and experiments; it offers no automated labelling capability for object detection images. It is tempting because annotation work happens inside notebooks, and would be correct when a data scientist needs a managed IDE to build and run custom code.
- ✗
SageMaker Data Wrangler
Why it's wrong here
Data Wrangler imports, transforms and prepares tabular and image data for analysis, but it does not generate bounding-box labels or manage labelling workforces. It is tempting because it sits in the data-preparation phase, and would be correct when cleaning and transforming datasets before training.
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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.