MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data science team wants to automate the retraining of a model whenever SageMaker Model Monitor detects a significant drift in data quality. They need the least amount of custom code. Which approach should they use?
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
✓
Set up a CloudWatch Alarm on the Model Monitor violation metric that sends a notification to an SNS topic, which triggers an AWS Lambda function to start a SageMaker pipeline
CloudWatch Alarms can be set on Model Monitor violation metrics. The alarm triggers an SNS topic that invokes a Lambda function to start a retraining pipeline. This requires minimal custom code.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Write a custom script on an EC2 instance that polls the Model Monitor endpoint every hour and triggers retraining if drift is detected
Why it's wrong here
Polling adds complexity and overhead; not the least amount of custom code.
- ✗
Configure an Amazon EventBridge rule that listens for Model Monitor violation events and directly triggers a SageMaker Pipeline execution
Why it's wrong here
Model Monitor does not emit violation events to EventBridge automatically; CloudWatch Alarms are the standard integration.
- ✓
Set up a CloudWatch Alarm on the Model Monitor violation metric that sends a notification to an SNS topic, which triggers an AWS Lambda function to start a SageMaker pipeline
Why this is correct
This uses native CloudWatch Alarms and SNS to invoke Lambda, which can start retraining. Minimal custom code needed.
- ✗
Use SageMaker Autopilot to automatically retrain the model when performance degrades
Why it's wrong here
Autopilot does not automatically trigger on drift; it requires manual invocation or scheduled jobs.
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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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.