- A
Use a pre-built annotation tool that enforces bounding box alignment
Tool constraints improve consistency.
- B
Use automated labeling with a pre-trained model
Why wrong: Auto-labeling quality depends on model; may not fix misalignment.
- C
Increase the number of workers per task
Why wrong: More workers may not align bounding boxes better.
- D
Adjust the confidence threshold for the model
Why wrong: Not applicable to labeling phase.
Question 986 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 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?
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
Option A is correct because 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.
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.
- ✓
Use a pre-built annotation tool that enforces bounding box alignment
Why this is correct
Tool constraints improve consistency.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Ground Truth's pre-built bounding box tool uses a canvas-based UI where users click and drag to define boxes, but without alignment enforcement, boxes can be off by several pixels. The tool can be configured with a 'snap-to-edge' feature that automatically aligns box edges to detected object boundaries using edge detection algorithms (e.g., Canny edge detection), reducing annotation variance. In real-world scenarios, this is critical for tasks like autonomous vehicle object detection, where even a 2-pixel misalignment can degrade model performance significantly.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a pre-built annotation tool that enforces bounding box alignment — Option A is correct because 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.
What should I do if I get this MLS-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
This MLS-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 MLS-C01 exam.
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