hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A healthcare company deploys a model to predict…
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.)
⚠ Common exam trap
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.
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
✓
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.
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.
- ✗
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.
- ✗
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.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.