MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team needs to automatically retrain a model when concept drift is detected in the deployed endpoint's predictions. Which TWO steps should they take? (Choose TWO.)
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
✓
Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Model Quality Monitor compares predictions with ground truth to detect concept drift. When an alarm triggers, a Lambda function can start a retraining pipeline. Data Quality Monitor is for data drift, not concept drift.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule retraining with Amazon EventBridge on a fixed schedule
Why it's wrong here
Schedule-based retraining does not respond to drift detection.
- ✓
Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Why this is correct
Alarm triggers retraining pipeline when quality drops.
- ✓
Set up SageMaker Model Monitor - Model Quality Monitor to compute prediction quality metrics against ground truth
Why this is correct
Model Quality Monitor detects concept drift by evaluating predictions against ground truth.
- ✗
Configure SageMaker Model Monitor - Data Quality Monitor to detect input drift
Why it's wrong here
Detects data drift, not concept drift.
- ✗
Use SageMaker Clarify to monitor bias drift
Why it's wrong here
Bias drift is unrelated to concept drift.
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 |
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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.