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MLA-C01 Practice Question: A company uses SageMaker Ground Truth to label a…
A company uses SageMaker Ground Truth to label a dataset for object detection. They set up a labeling job with a private workforce. After labeling, they export the dataset and train a model using SageMaker's built-in object detection algorithm. The model achieves high accuracy on the test set but low accuracy on a small holdout set that was manually labeled by an expert. What might be the issue?
⚠ Common exam trap
Candidates often assume 'high accuracy on the test set' always indicates a good model, but the question tests the understanding that label quality from Ground Truth (especially with low worker consensus) can create a false sense of performance when the test set shares the same labeling errors.
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
✓
The labeling job had insufficient worker consensus.
Low worker consensus in a Ground Truth labeling job indicates inconsistent annotations among workers, leading to noisy labels. When the model trains on these inconsistent labels, it learns patterns that may not generalize to a clean, expert-labeled holdout set, causing a significant accuracy drop despite high performance on the test set (which likely shares the same labeling noise). Ground Truth uses a 'consensus' mechanism to finalize labels, and insufficient consensus means the final labels may be unreliable for training a robust object detection model.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The dataset size is too small.
Why it's wrong here
Small dataset would cause overfitting and poor performance on both sets.
- ✗
The object detection algorithm is not suitable.
Why it's wrong here
SageMaker's built-in object detection is suitable for the task.
- ✗
The holdout set uses a different labeling schema.
Why it's wrong here
Different schema would cause consistent mislabeling, but the test set may also be affected.
- ✓
The labeling job had insufficient worker consensus.
Why this is correct
Correct: Low consensus leads to noisy training labels, degrading model quality.
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Written by Johnson Ajibi, MSc IT Security
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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.