- A
Schedule retraining with Amazon EventBridge on a fixed schedule
Why wrong: Schedule-based retraining does not respond to drift detection.
- B
Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Alarm triggers retraining pipeline when quality drops.
- C
Set up SageMaker Model Monitor - Model Quality Monitor to compute prediction quality metrics against ground truth
Model Quality Monitor detects concept drift by evaluating predictions against ground truth.
- D
Configure SageMaker Model Monitor - Data Quality Monitor to detect input drift
Why wrong: Detects data drift, not concept drift.
- E
Use SageMaker Clarify to monitor bias drift
Why wrong: Bias drift is unrelated to concept drift.
Question 962 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 machine learning team needs to automatically retrain a model when concept drift is detected in the deployed endpoint's predictions. Which TWO steps should they take? (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 CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Model Quality Monitor compares predictions with ground truth to detect concept drift. When an alarm triggers, a Lambda function can start a retraining pipeline. Data Quality Monitor is for data drift, not concept drift.
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.
- ✗
Schedule retraining with Amazon EventBridge on a fixed schedule
Why it's wrong here
Schedule-based retraining does not respond to drift detection.
- ✓
Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Why this is correct
Alarm triggers retraining pipeline when quality drops.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Set up SageMaker Model Monitor - Model Quality Monitor to compute prediction quality metrics against ground truth
Why this is correct
Model Quality Monitor detects concept drift by evaluating predictions against ground truth.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Configure SageMaker Model Monitor - Data Quality Monitor to detect input drift
Why it's wrong here
Detects data drift, not concept drift.
- ✗
Use SageMaker Clarify to monitor bias drift
Why it's wrong here
Bias drift is unrelated to concept drift.
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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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 CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job — Model Quality Monitor compares predictions with ground truth to detect concept drift. When an alarm triggers, a Lambda function can start a retraining pipeline. Data Quality Monitor is for data drift, not concept drift.
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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