- A
Use a smaller instance type for the labeling job.
Why wrong: Instance type affects throughput but not labeling cost directly; smaller instances may be slower.
- B
Use a smaller workforce type.
Why wrong: Workforce type affects labeling quality and speed, not necessarily cost-effectiveness.
- C
Set up a labeling workflow with 'Incremental training'.
Incremental training leverages existing models to reduce labeling needs.
- D
Enable the 'Consolidated billing' for labeling costs.
Why wrong: Consolidated billing is an organizational feature, not a cost-saving measure for individual jobs.
- E
Use the 'Automated data labeling' feature.
Automated labeling reduces manual effort and cost.
Question 578 of 1,000
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 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
✓
Set up a labeling workflow with 'Incremental training'.
Option C is correct because 'Incremental training' allows you to start with a smaller initial labeled dataset, train a model, and then use that model to pre-label additional data, which reduces the amount of manual labeling required. This directly lowers labeling costs by minimizing human effort. Option E is correct because 'Automated data labeling' uses a trained model to automatically generate labels for unlabeled data, significantly reducing the need for human labelers and thus cutting 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.
- ✗
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
AWS often tests the misconception that reducing compute instance size or workforce type directly lowers labeling costs, when in reality, Ground Truth costs are driven by the number of human annotations and the use of automated labeling features.
Detailed technical explanation
How to think about this question
Incremental training in SageMaker Ground Truth leverages a process called 'active learning', where the model iteratively selects the most uncertain or informative samples for human labeling, maximizing label efficiency. Automated data labeling uses a 'confidence threshold' mechanism: the model automatically labels data points where its prediction confidence exceeds a set threshold (e.g., 90%), and only sends low-confidence samples to human labelers. This hybrid approach can reduce labeling costs by up to 70% in real-world scenarios, especially for large datasets with high redundancy.
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.
- →
ML Solution Monitoring, Maintenance and Security — study guide chapter
Learn the concepts, then practise the questions
- →
ML Solution Monitoring, Maintenance and Security practice questions
Targeted practice on this topic area only
- →
All MLA-C01 questions
1,000 questions across all exam domains
- →
AWS Certified Machine Learning Engineer Associate MLA-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLA-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLA-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
ML Model Development practice questions
Practise MLA-C01 questions linked to ML Model Development.
Data Preparation for Machine Learning practice questions
Practise MLA-C01 questions linked to Data Preparation for Machine Learning.
Deployment and Orchestration of ML Workflows practice questions
Practise MLA-C01 questions linked to Deployment and Orchestration of ML Workflows.
ML Solution Monitoring, Maintenance, and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance, and Security.
ML Solution Monitoring, Maintenance and Security practice questions
Practise MLA-C01 questions linked to ML Solution Monitoring, Maintenance and Security.
MLA-C01 fundamentals practice questions
Practise MLA-C01 questions linked to MLA-C01 fundamentals.
MLA-C01 scenario practice questions
Practise MLA-C01 questions linked to MLA-C01 scenario.
MLA-C01 troubleshooting practice questions
Practise MLA-C01 questions linked to MLA-C01 troubleshooting.
Practice this exam
Start a free MLA-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLA-C01 question test?
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'. — Option C is correct because 'Incremental training' allows you to start with a smaller initial labeled dataset, train a model, and then use that model to pre-label additional data, which reduces the amount of manual labeling required. This directly lowers labeling costs by minimizing human effort. Option E is correct because 'Automated data labeling' uses a trained model to automatically generate labels for unlabeled data, significantly reducing the need for human labelers and thus cutting 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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Keep practising
More MLA-C01 practice questions
- A team is using SageMaker Pipelines to train a model. The pipeline has multiple steps: data processing, training, evalua…
- A machine learning team deploys a custom container image for an Amazon SageMaker training job. The container needs to ac…
- A machine learning engineer sees the above error in Amazon CloudWatch Logs for a SageMaker endpoint. What is the most li…
- A data scientist has trained a model that achieves 95% accuracy on the training set but only 70% on the test set. Which…
- Refer to the exhibit. A data scientist reviews the output of a SageMaker training job. The model has 95% training accura…
- A team is using Amazon SageMaker to train a neural network. They want to minimize training time while effectively explor…
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.