MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company uses SageMaker Ground Truth to label images for object detection. After labeling, they notice that the bounding boxes are often misaligned with the objects. Which action should they take to improve label quality?
⚠ Common exam trap
It's easy for candidates to confuse label quality improvement strategies (e.g., using consensus voting or automated labeling) with the specific need to enforce geometric precision in bounding box annotations, leading them to select options that address general accuracy rather than alignment accuracy.
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
✓
Use a pre-built annotation tool that enforces bounding box alignment
SageMaker Ground Truth offers pre-built annotation tools, such as the bounding box tool, which includes features like snap-to-grid or edge alignment that enforce precise bounding box placement. Using this tool directly improves label quality by reducing human error in manual drawing, ensuring boxes tightly fit objects without manual guesswork.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a pre-built annotation tool that enforces bounding box alignment
Why this is correct
Tool constraints improve consistency.
- ✗
Use automated labeling with a pre-trained model
Why it's wrong here
Auto-labeling quality depends on model; may not fix misalignment.
- ✗
Increase the number of workers per task
Why it's wrong here
More workers may not align bounding boxes better.
- ✗
Adjust the confidence threshold for the model
Why it's wrong here
Not applicable to labeling phase.
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