- A
The dataset size is too small.
Why wrong: Small dataset would cause overfitting and poor performance on both sets.
- B
The object detection algorithm is not suitable.
Why wrong: SageMaker's built-in object detection is suitable for the task.
- C
The holdout set uses a different labeling schema.
Why wrong: Different schema would cause consistent mislabeling, but the test set may also be affected.
- D
The labeling job had insufficient worker consensus.
Correct: Low consensus leads to noisy training labels, degrading model quality.
Question 429 of 1,000
mediumMultiple ChoiceObjective-mapped
Improving Model Accuracy with Labeling Consensus
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Option D is correct because 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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may 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.
Detailed technical explanation
How to think about this question
Ground Truth's private workforce uses a 'worker task template' and 'annotation consolidation' logic (e.g., majority voting or 'consensus' threshold) to produce final labels. If the consensus threshold is set too low (e.g., 51% agreement), the final labels may include ambiguous or incorrect annotations, especially for object detection bounding boxes where slight variations in coordinates can introduce noise. This noise can cause the model to overfit to the labeling inconsistencies, resulting in high accuracy on a noisy test set but poor performance on a clean, expert-labeled holdout set—a classic symptom of label noise.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: The labeling job had insufficient worker consensus. — Option D is correct because 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.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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