- A
Active learning
Active learning selects the most uncertain or informative samples for labeling, minimizing cost while maximizing model improvement.
- B
Annotation consolidation
Why wrong: Annotation consolidation merges multiple annotations into one, but does not actively reduce the number of images to label.
- C
Data labeling workforce management
Why wrong: Workforce management helps assign tasks to workers but does not select which data to label.
- D
Consolidated labeling
Why wrong: Consolidated labeling refers to combining annotations from multiple workers, not reducing the number of labels needed.
Question 775 of 1,000
easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An ML team wants to use Amazon SageMaker Ground…
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.
An ML team wants to use Amazon SageMaker Ground Truth to create a labeled dataset for a multi-class image classification task. They have a large set of unlabeled images and want to minimize labeling costs while maintaining high accuracy. Which Ground Truth 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
Active learning in SageMaker Ground Truth automatically selects the most informative unlabeled images for human labeling, reducing the total number of labels needed while maintaining model accuracy. By iteratively training a model on a small labeled subset and then using that model to identify uncertain predictions, the system focuses labeling effort on the data that will most improve the model, directly minimizing labeling costs.
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
Why this is correct
Active learning selects the most uncertain or informative samples for labeling, minimizing cost while maximizing model improvement.
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.
- ✗
Annotation consolidation
Why it's wrong here
Annotation consolidation merges multiple annotations into one, but does not actively reduce the number of images to label.
- ✗
Data labeling workforce management
Why it's wrong here
Workforce management helps assign tasks to workers but does not select which data to label.
- ✗
Consolidated labeling
Why it's wrong here
Consolidated labeling refers to combining annotations from multiple workers, not reducing the number of labels needed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'annotation consolidation' (a post-labeling quality step) with a cost-reduction feature, or think that workforce management alone reduces costs, when in fact active learning is the specific feature designed to minimize the number of labels required.
Detailed technical explanation
How to think about this question
Active learning in SageMaker Ground Truth uses a selection strategy (e.g., uncertainty sampling or margin sampling) to choose images where the model's prediction confidence is low. Under the hood, it trains a preliminary model on a small labeled dataset, then scores the unlabeled pool, and only sends the most ambiguous images to human labelers, which can reduce labeling costs by up to 50% compared to random sampling. A real-world scenario is a medical imaging dataset where most images are normal; active learning ensures only the rare, ambiguous cases are labeled, saving significant annotation budget.
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 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 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: Active learning — Active learning in SageMaker Ground Truth automatically selects the most informative unlabeled images for human labeling, reducing the total number of labels needed while maintaining model accuracy. By iteratively training a model on a small labeled subset and then using that model to identify uncertain predictions, the system focuses labeling effort on the data that will most improve the model, directly minimizing labeling costs.
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.
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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