- A
Use SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining
Model Quality Monitor evaluates predictions against ground truth; CloudWatch alarm on quality metric triggers retraining.
- B
Manually evaluate the model weekly and retrain as needed
Why wrong: Manual process is not automated and may miss degradation between evaluations.
- C
Use SageMaker Model Monitor - Data Quality Monitor to detect drift, then trigger retraining
Why wrong: Data quality monitor detects input drift, not prediction quality. Ground truth is required for model quality.
- D
Use SageMaker Clarify to monitor bias drift and trigger retraining
Why wrong: Bias drift monitors fairness, not overall prediction quality.
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 observes that model performance on a SageMaker endpoint has degraded over the past week. Ground truth labels are available with a 2-day delay. The engineer wants to automatically trigger a retraining pipeline when prediction quality drops below an acceptable threshold. Which approach is most appropriate?
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
Use SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining
Option A is correct because SageMaker Model Monitor's Model Quality Monitor is specifically designed to compare model predictions against ground truth labels (available with a 2-day delay) and track metrics like accuracy, precision, recall, or F1 score. You can configure a CloudWatch alarm on a metric such as 'accuracy' dropping below a threshold, which triggers an AWS Lambda function to start the retraining pipeline. This automates the detection of prediction quality degradation and the retraining response without manual intervention.
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 SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining
Why this is correct
Model Quality Monitor evaluates predictions against ground truth; CloudWatch alarm on quality metric triggers retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Manually evaluate the model weekly and retrain as needed
Why it's wrong here
Manual process is not automated and may miss degradation between evaluations.
- ✗
Use SageMaker Model Monitor - Data Quality Monitor to detect drift, then trigger retraining
Why it's wrong here
Data quality monitor detects input drift, not prediction quality. Ground truth is required for model quality.
- ✗
Use SageMaker Clarify to monitor bias drift and trigger retraining
Why it's wrong here
Bias drift monitors fairness, not overall prediction quality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse Data Quality Monitor (which monitors input data drift) with Model Quality Monitor (which monitors prediction accuracy against ground truth), leading them to choose Option C incorrectly.
Detailed technical explanation
How to think about this question
Model Quality Monitor works by scheduling baseline and ongoing monitoring jobs that compute metrics like accuracy, F1, or custom metrics from ground truth labels. It uses CloudWatch Metrics to expose these values, and you can set an alarm on a metric like 'accuracy' with a threshold (e.g., < 0.85) to trigger an SNS topic or Lambda. A real-world scenario is a fraud detection model where ground truth (fraud confirmed) arrives after 48 hours; Model Quality Monitor can detect a drop in precision and automatically retrain the model to adapt to new fraud patterns.
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.
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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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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: Use SageMaker Model Monitor - Model Quality Monitor with ground truth, create a CloudWatch alarm on the metric, and trigger an AWS Lambda function to start retraining — Option A is correct because SageMaker Model Monitor's Model Quality Monitor is specifically designed to compare model predictions against ground truth labels (available with a 2-day delay) and track metrics like accuracy, precision, recall, or F1 score. You can configure a CloudWatch alarm on a metric such as 'accuracy' dropping below a threshold, which triggers an AWS Lambda function to start the retraining pipeline. This automates the detection of prediction quality degradation and the retraining response without manual intervention.
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
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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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