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MLA-C01 Practice Question: A company uses Amazon SageMaker Ground Truth to…

A company uses Amazon SageMaker Ground Truth to label images for object detection. They want to minimize labeling costs while maintaining high accuracy. Which feature should they enable?

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

Active learning to automatically select samples for labeling

Active learning in Ground Truth selects the most informative images for labeling, reducing the number of labels needed while maintaining model quality.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Active learning to automatically select samples for labeling

    Why this is correct

    Active learning prioritizes samples where the model is uncertain, reducing labeling effort.

  • Use of mechanical turk for all labeling

    Why it's wrong here

    Using a workforce without active learning can be more expensive.

  • Pre-built annotation workflows for bounding boxes

    Why it's wrong here

    Pre-built workflows improve efficiency but do not reduce the number of labels needed.

  • Automated data labeling with AWS Lambda

    Why it's wrong here

    Lambda does not provide intelligent sample selection; it just automates tasks.

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JA

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.