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MLA-C01 Practice Question: A company uses Amazon SageMaker Ground Truth to…
A company uses Amazon SageMaker Ground Truth to create a labeled dataset. They want to monitor the accuracy of human labelers during the labeling process. Which metric should they track?
⚠ Common exam trap
MLA-C01 often tests the distinction between operational metrics (cost, throughput, acceptance rate) and quality metrics (accuracy vs. blinded ground truth) — candidates pick 'task acceptance rate' because it sounds like a quality signal, but it measures workflow behavior, not correctness.
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
✓
Accuracy against blinded ground truth
Ground Truth supports a built-in quality control mechanism where a percentage of tasks are sent to multiple workers, and one worker's answer is treated as the 'ground truth' (blinded to the others). By comparing each labeler's output against this blinded ground truth, the labeling job computes per-worker accuracy metrics, which is the direct measure of labeler quality. This is the metric designed specifically to monitor human labeler accuracy during the job.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Labeling job cost
Why it's wrong here
Labelling job cost reports spend on human annotators, which says nothing about whether their labels are correct. It is tempting because cost monitoring is a genuine Ground Truth concern, and it would be the right metric when budgeting a labelling project or comparing pricing tiers, not when validating label quality.
- ✗
Number of tasks completed
Why it's wrong here
Quantity does not measure quality.
- ✓
Accuracy against blinded ground truth
Why this is correct
Blinded ground truth compares each labeler's output against hidden known-correct labels, yielding a per-worker accuracy score. This directly satisfies the requirement to monitor labeler accuracy during labelling, unlike throughput or time-per-task metrics, which measure speed rather than correctness.
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Task acceptance rate
Why it's wrong here
Task acceptance rate measures how often workers accept assigned tasks, not whether their labels match ground truth. It is tempting because it flags unreliable workers, but it belongs to workforce-management workflows. Accuracy during labelling is tracked through annotation-consistency metrics such as label agreement against known answers.
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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.