The answer is an AWS Lambda function that starts a SageMaker training job or pipeline execution, a CloudWatch alarm monitoring a SageMaker Model Monitor quality metric, and a SageMaker Model Monitor schedule that captures inference data and computes metrics. These three components form the core of automated retraining on performance degradation because the Model Monitor schedule continuously tracks model performance, the CloudWatch alarm detects when a metric like accuracy drops below a defined threshold, and the Lambda function triggers the retraining workflow in response to the alarm state. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of event-driven MLOps pipelines, often appearing as a multi-select question where a common trap is choosing a manual approval step or a static endpoint update instead of the automated trigger chain. Remember the sequence: Monitor detects, Alarm alerts, Lambda acts—or simply think “MAL” for Monitor, Alarm, Lambda.
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.
Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
A CloudWatch alarm that triggers when a quality metric falls below a threshold.
Option B is correct because a CloudWatch alarm can monitor a SageMaker Model Monitor quality metric (e.g., accuracy, precision) and trigger an alarm when the metric falls below a defined threshold. This alarm acts as the event source to initiate automated retraining, forming the monitoring and alerting backbone of the retraining pipeline.
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.
✗
An Amazon SNS topic with a subscription to send a manual approval email.
Why it's wrong here
Manual approval is not automated retraining.
✓
A CloudWatch alarm that triggers when a quality metric falls below a threshold.
Why this is correct
The alarm detects degradation and triggers the retraining.
Related concept
Read the scenario before looking for a memorised answer.
✓
A SageMaker Model Monitor schedule to capture inference data and compute quality metrics.
Why this is correct
Model Monitor provides the metrics to detect degradation.
Related concept
Read the scenario before looking for a memorised answer.
✓
An AWS Lambda function that starts a SageMaker training job or pipeline execution.
Why this is correct
Lambda can orchestrate the retraining process.
Related concept
Read the scenario before looking for a memorised answer.
✗
A production variant with a canary traffic shift configuration.
Why it's wrong here
Canary is for gradual deployment, not required for retraining.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the monitoring and alerting components (CloudWatch alarm and Model Monitor) with deployment or notification mechanisms, mistakenly selecting manual approval (SNS) or traffic shifting (canary) as part of the automated retraining workflow.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Monitor uses a built-in or custom baseline to compute statistics on inference data, then publishes metrics to CloudWatch. When the CloudWatch alarm state changes to ALARM, it can invoke an AWS Lambda function via SNS or directly via CloudWatch Events, which then starts a SageMaker training job or pipeline. A real-world scenario might involve a model that drifts due to seasonal data changes, where the alarm triggers retraining on new data without any human delay.
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
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.
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 — 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 CloudWatch alarm that triggers when a quality metric falls below a threshold. — Option B is correct because a CloudWatch alarm can monitor a SageMaker Model Monitor quality metric (e.g., accuracy, precision) and trigger an alarm when the metric falls below a defined threshold. This alarm acts as the event source to initiate automated retraining, forming the monitoring and alerting backbone of the retraining pipeline.
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 →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer is setting up automated retraining for a model using SageMaker Pipelines. The pipeline should trigger when a data drift alert is received from Model Monitor. Which event source should the engineer use to initiate the pipeline?
hard
✓ A.Amazon CloudWatch Events (Amazon EventBridge) rule that captures Model Monitor outcome.
B.AWS Lambda function that polls CloudWatch logs.
C.S3 event notification on the monitoring output bucket.
D.SageMaker model monitor webhook.
Why A: Option D is correct because SageMaker Model Monitor publishes drift results as Amazon CloudWatch Events (now EventBridge). Option A: S3 events on monitoring output are not directly linked to drift alerts. Option B: no webhook exists. Option C: polling is inefficient.
Variation 2. A team wants to automatically retrain a model when new labeled data arrives. Which SageMaker feature can orchestrate this workflow?
easy
✓ A.SageMaker Pipelines
B.SageMaker Model Monitor
C.SageMaker Debugger
D.SageMaker Autopilot
Why A: SageMaker Pipelines is a workflow orchestration service that can automate retraining pipelines. Model Monitor detects drift, Debugger debugs training, and Autopilot automates model building.
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Question Discussion
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