- A
SHAP values for feature attribution
Why wrong: SHAP values are used for feature attribution drift monitoring, not model quality.
- B
A constraints file with allowed deviation thresholds
Why wrong: Constraints file is used for data quality monitoring to define acceptable drift thresholds.
- C
A ground truth labels dataset for comparison
Ground truth labels are essential to compare against predictions and compute model quality metrics.
- D
The endpoint's prediction output captured in real-time
Model Monitor captures predictions from the endpoint to compare against ground truth.
- E
A baseline statistics file derived from the training data
Why wrong: Baseline statistics are required for data quality monitoring, not model quality.
Question 147 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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 has deployed a model to a SageMaker real-time endpoint and wants to set up automated monitoring for model quality. Which TWO elements are required to configure SageMaker Model Monitor for model quality? (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
✓
A ground truth labels dataset for comparison
Option C is correct because SageMaker Model Monitor for model quality requires a ground truth labels dataset to compare the model's predictions against actual outcomes. This comparison is essential for calculating quality metrics like accuracy, precision, recall, or F1 score, which indicate how well the model is performing over time.
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.
- ✗
SHAP values for feature attribution
Why it's wrong here
SHAP values are used for feature attribution drift monitoring, not model quality.
- ✗
A constraints file with allowed deviation thresholds
Why it's wrong here
Constraints file is used for data quality monitoring to define acceptable drift thresholds.
- ✓
A ground truth labels dataset for comparison
Why this is correct
Ground truth labels are essential to compare against predictions and compute model quality metrics.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
The endpoint's prediction output captured in real-time
Why this is correct
Model Monitor captures predictions from the endpoint to compare against ground truth.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A baseline statistics file derived from the training data
Why it's wrong here
Baseline statistics are required for data quality monitoring, not model quality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse the requirements for model quality monitoring (which needs ground truth labels and captured predictions) with those for data quality monitoring (which needs a baseline statistics file and constraints), leading them to select options B or E incorrectly.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor for model quality works by capturing real-time inference requests and responses from the endpoint (Option D), then comparing those predictions against a ground truth labels dataset (Option C) that you provide, typically from a separate data source like a data warehouse or manual labeling pipeline. The monitoring job runs on a schedule (e.g., hourly) and computes metrics like accuracy or F1 score, triggering alerts if performance degrades below a threshold you define. This is distinct from data quality monitoring, which uses a baseline statistics file and constraints to detect drift in input features.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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?
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: A ground truth labels dataset for comparison — Option C is correct because SageMaker Model Monitor for model quality requires a ground truth labels dataset to compare the model's predictions against actual outcomes. This comparison is essential for calculating quality metrics like accuracy, precision, recall, or F1 score, which indicate how well the model is performing over time.
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.
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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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