The answer is to use the 'Automated data labeling' feature and to leverage incremental training with an existing model. Automated data labeling directly reduces labeling costs by having a trained model predict labels for high-confidence data, leaving only ambiguous items for human review, which drastically cuts the volume of manual work. Incremental training further lowers costs by starting from a pre-trained model, requiring fewer labeled examples to reach the desired accuracy and thus reducing the total number of items needing manual annotation. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this question tests your understanding of cost-optimization strategies within SageMaker Ground Truth, a common scenario for the "ML Solutions" domain. A common trap is selecting "use a larger workforce" or "increase the number of labelers," which actually raises costs. Remember the memory tip: "Auto-label the easy stuff, incrementally train the model" to keep costs low.
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. 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 team uses SageMaker Ground Truth to create labeled datasets. They need to ensure labeling jobs are cost-effective. Which TWO measures should they take? (Select TWO.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Set up a labeling workflow with 'Incremental training'.
Automated data labeling reduces manual labeling cost by using model predictions to label data, and incremental training reduces the number of items that need manual labeling by starting from an existing 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.
✗
Use a smaller instance type for the labeling job.
Why it's wrong here
Instance type affects throughput but not labeling cost directly; smaller instances may be slower.
✗
Use a smaller workforce type.
Why it's wrong here
Workforce type affects labeling quality and speed, not necessarily cost-effectiveness.
✓
Set up a labeling workflow with 'Incremental training'.
Why this is correct
Incremental training leverages existing models to reduce labeling needs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Enable the 'Consolidated billing' for labeling costs.
Why it's wrong here
Consolidated billing is an organizational feature, not a cost-saving measure for individual jobs.
✓
Use the 'Automated data labeling' feature.
Why this is correct
Automated labeling reduces manual effort and cost.
Related concept
Read the scenario before looking for a memorised answer.
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.
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set up a labeling workflow with 'Incremental training'. — Automated data labeling reduces manual labeling cost by using model predictions to label data, and incremental training reduces the number of items that need manual labeling by starting from an existing model.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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.
Question Discussion
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