hardMultiple Select
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
Option A is correct because SageMaker Model Monitor is the AWS service designed to continuously capture endpoint data and evaluate it against baselines, emitting CloudWatch metrics and alarms when data quality, model quality, bias, or drift violations occur, which is exactly what detecting concept drift requires. Option C is correct because Model Monitor's model quality monitoring depends on ground truth labels being joined to captured predictions; for readmission risk, those labels come from actual patient outcomes, so a labeling/feedback pipeline is essential to measure real drift. Option E is correct because the automated retraining requirement is met by triggering a SageMaker training job programmatically — for example, a Lambda function invoked by a CloudWatch alarm on the drift metric — which closes the detect-and-retrain loop. Option B is not appropriate because disabling the endpoint would halt predictions for clinicians and is unnecessary; retraining can occur while the existing endpoint continues serving, with a new model version deployed afterward. Option D is not appropriate because weekly manual comparison against a holdout set is neither automated nor a production drift-detection mechanism, and it would not satisfy the requirement to automatically retrain when drift is detected.
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
SageMaker Model Monitor continuously captures endpoint data and compares it against a baseline, emitting CloudWatch metrics when drift is detected. This satisfies the requirement to detect drift, providing the trigger signal that downstream automation uses to initiate retraining.
- ✗
Disable the existing endpoint to prevent stale predictions during retraining
Why it's wrong here
Disabling the endpoint halts serving entirely, so the solution cannot detect drift on live traffic or retrain automatically; the endpoint must stay available while monitoring runs. It is tempting because pausing stale predictions sounds prudent, and it would be correct if the requirement were preventing outdated inference during a planned outage.
- ✓
Set up a process to collect ground truth labels from patient outcomes
Why this is correct
Concept drift detection requires comparing predictions against actual outcomes, so ground truth labels from patient readmission results must be collected and merged with captured inference data. Without these labels, Model Monitor cannot compute accuracy or bias metrics that reveal drift.
- ✗
Manually compare the model's predictions against a holdout validation set each week
Why it's wrong here
Weekly manual comparison is neither automated drift detection nor automatic retraining, and it cannot scale or trigger remediation. It is tempting because holdout evaluation genuinely measures model quality, and it would be correct for periodic offline validation rather than the continuous monitored pipeline the scenario demands.
- ✓
Use AWS Lambda to invoke a SageMaker training job when drift is detected
Why this is correct
AWS Lambda provides the event-driven compute that satisfies the automatic retraining constraint: when a drift-detection alarm fires, the function invokes a SageMaker training job without manual intervention. This closes the detect-and-retrain loop the scenario requires, since SageMaker training jobs are the native mechanism for producing an updated model artefact.
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 |
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 665-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
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.