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MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security

A machine learning team uses SageMaker Pipelines and wants to automatically retrain a model when data drift is detected. They have set up Model Monitor to publish drift violations to CloudWatch. Which approach provides a COMPLETE serverless retraining pipeline triggered by drift detection?

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

Configure a CloudWatch Alarm on drift metric → SNS topic → Lambda function that starts the SageMaker Pipeline execution

The recommended pattern: CloudWatch Alarm triggers on drift metric → SNS message → Lambda function (receives SNS) → starts SageMaker Pipeline execution. EventBridge could also trigger on SNS events, but Lambda is simplest. EventBridge can schedule retraining but does not directly react to specific drift alarms. Step Functions would add unnecessary complexity.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Use SageMaker Model Monitor to directly invoke a SageMaker Pipeline when drift is detected

    Why it's wrong here

    Model Monitor cannot directly invoke pipelines; it only publishes metrics and violations to CloudWatch.

  • Use EventBridge to schedule retraining daily regardless of drift

    Why it's wrong here

    Scheduled retraining does not react to drift events; it runs on a fixed schedule.

  • Configure a CloudWatch Alarm on drift metric → SNS topic → Lambda function that starts the SageMaker Pipeline execution

    Why this is correct

    This chain fully automates retraining on drift detection without manual intervention.

  • Create an EventBridge rule that triggers on Model Monitor drift events to start the pipeline

    Why it's wrong here

    Model Monitor does not emit custom events to EventBridge; it uses CloudWatch metrics/alarms.

Quick reference

Cloud Service Model Comparison

ModelYou ManageProvider ManagesExamples
IaaSOS, runtime, apps, dataHardware, hypervisor, networkingEC2, Azure VMs, GCP Compute Engine
PaaSApps and dataOS, runtime, middleware, hardwareElastic Beanstalk, Azure App Service
SaaSData and settings onlyEverything elseMicrosoft 365, Salesforce, Workday
FaaS / ServerlessFunction code onlyInfra, scaling, runtimeLambda, Azure Functions, Cloud Run
CaaSContainers and appsKubernetes, OS, hardwareEKS, 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.