- A
Create a CloudWatch Alarm for accuracy degradation
Why wrong: CloudWatch Alarms can be set up after monitoring, but they are not a required step for configuring the monitoring itself.
- B
Use Amazon SageMaker Clarify for bias detection
Why wrong: Clarify is for bias monitoring, not model quality.
- C
Create a schedule for the monitoring job to run at regular intervals
A schedule defines how often the monitoring job runs to compare predictions against ground truth.
- D
Create a baseline for model quality using training data and ground truth labels
A baseline establishes expected performance metrics (e.g., accuracy, precision) for comparison.
- E
Enable data capture on the endpoint to collect predictions
Why wrong: Data capture is needed for predictions, but it is set up when creating the endpoint, not as part of configuring model quality monitoring.
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.
A machine learning engineer is setting up model quality monitoring for a binary classification model. They have ground truth labels available in Amazon S3. Which TWO steps are required to configure model quality monitoring? (Choose 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
Create a schedule for the monitoring job to run at regular intervals
A baseline must be computed from training data and ground truth, and a schedule for monitoring jobs must be defined. The monitoring job then compares production predictions against ground truth.
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.
- ✗
Create a CloudWatch Alarm for accuracy degradation
Why it's wrong here
CloudWatch Alarms can be set up after monitoring, but they are not a required step for configuring the monitoring itself.
- ✗
Use Amazon SageMaker Clarify for bias detection
Why it's wrong here
Clarify is for bias monitoring, not model quality.
- ✓
Create a schedule for the monitoring job to run at regular intervals
Why this is correct
A schedule defines how often the monitoring job runs to compare predictions against ground truth.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a baseline for model quality using training data and ground truth labels
Why this is correct
A baseline establishes expected performance metrics (e.g., accuracy, precision) for comparison.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable data capture on the endpoint to collect predictions
Why it's wrong here
Data capture is needed for predictions, but it is set up when creating the endpoint, not as part of configuring model quality monitoring.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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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: Create a schedule for the monitoring job to run at regular intervals — A baseline must be computed from training data and ground truth, and a schedule for monitoring jobs must be defined. The monitoring job then compares production predictions against ground truth.
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.
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 →
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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