- A
Active learning to automatically select samples for labeling
Active learning prioritizes samples where the model is uncertain, reducing labeling effort.
- B
Use of mechanical turk for all labeling
Why wrong: Using a workforce without active learning can be more expensive.
- C
Pre-built annotation workflows for bounding boxes
Why wrong: Pre-built workflows improve efficiency but do not reduce the number of labels needed.
- D
Automated data labeling with AWS Lambda
Why wrong: Lambda does not provide intelligent sample selection; it just automates tasks.
Question 678 of 1,000
mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company uses Amazon SageMaker Ground Truth to…
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 Amazon SageMaker Ground Truth to label images for object detection. They want to minimize labeling costs while maintaining high accuracy. Which feature should they enable?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Active learning to automatically select samples for labeling
Active learning in Ground Truth selects the most informative images for labeling, reducing the number of labels needed while maintaining model quality.
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.
- ✓
Active learning to automatically select samples for labeling
Why this is correct
Active learning prioritizes samples where the model is uncertain, reducing labeling effort.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use of mechanical turk for all labeling
Why it's wrong here
Using a workforce without active learning can be more expensive.
- ✗
Pre-built annotation workflows for bounding boxes
Why it's wrong here
Pre-built workflows improve efficiency but do not reduce the number of labels needed.
- ✗
Automated data labeling with AWS Lambda
Why it's wrong here
Lambda does not provide intelligent sample selection; it just automates tasks.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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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: Active learning to automatically select samples for labeling — Active learning in Ground Truth selects the most informative images for labeling, reducing the number of labels needed while maintaining model quality.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jul 4, 2026
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.
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