The answer is to use Amazon SageMaker Model Monitor with AWS Lambda triggering automatic retraining. This combination is correct because SageMaker Model Monitor continuously tracks prediction quality metrics like accuracy and precision against a baseline, enabling automated concept drift detection and model retraining in SageMaker without manual oversight. When drift is detected, a Lambda function invokes a new SageMaker training job, creating a closed-loop pipeline that keeps the model current. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of the MLOps lifecycle—specifically how to integrate monitoring, alerting, and retraining using managed services. A common trap is choosing manual retraining steps or relying solely on data drift metrics, but the question emphasizes prediction quality drift. Memory tip: think “Monitor, Lambda, Train” as the three-step chain for automated drift response.
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 healthcare company deploys a model to predict patient readmission risk. The model was trained on historical data and is now showing signs of concept drift. The team needs to implement a monitoring solution that can detect drift and automatically retrain the model when drift is detected. Which THREE steps should the team take to build this solution? (Choose THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Deploy SageMaker Model Monitor to track prediction quality over time
A is correct because Amazon SageMaker Model Monitor can continuously track prediction quality metrics (e.g., accuracy, precision) over time by analyzing data captured from the endpoint. This allows the team to detect concept drift by comparing live predictions against a baseline, triggering alerts when performance degrades. It provides a managed, automated way to monitor model quality 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.
✓
Deploy SageMaker Model Monitor to track prediction quality over time
Why this is correct
Model Monitor can detect drift using ground truth.
Related concept
Read the scenario before looking for a memorised answer.
✗
Disable the existing endpoint to prevent stale predictions during retraining
Why it's wrong here
Disabling the endpoint would cause downtime; use a blue/green deployment instead.
✓
Set up a process to collect ground truth labels from patient outcomes
Why this is correct
Ground truth is required to detect concept drift.
Related concept
Read the scenario before looking for a memorised answer.
✗
Manually compare the model's predictions against a holdout validation set each week
Why it's wrong here
Manual comparison is not automated and doesn't scale.
✓
Use AWS Lambda to invoke a SageMaker training job when drift is detected
Why this is correct
Lambda can automate the retraining trigger.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates might think disabling the endpoint (Option B) is necessary to prevent stale predictions, but AWS best practice is to keep the endpoint live and use a separate pipeline (e.g., Lambda triggering a training job) to retrain and then update the endpoint without downtime.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Model Monitor uses a baseline (e.g., statistics from training data) and compares it against live inference data captured in real time via the Data Capture feature. It can compute metrics like feature distribution drift (using KL divergence or Earth Mover's Distance) and model quality metrics (e.g., F1 score) when ground truth is available. In a real-world scenario, a healthcare model predicting readmission risk might see drift due to changes in patient demographics or treatment protocols, and automated retraining via Lambda ensures the model adapts without manual oversight.
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.
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: Deploy SageMaker Model Monitor to track prediction quality over time — A is correct because Amazon SageMaker Model Monitor can continuously track prediction quality metrics (e.g., accuracy, precision) over time by analyzing data captured from the endpoint. This allows the team to detect concept drift by comparing live predictions against a baseline, triggering alerts when performance degrades. It provides a managed, automated way to monitor model quality 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.
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Question Discussion
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