MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to implement a retraining pipeline that automatically triggers when SageMaker Model Monitor detects data drift. The retraining job should use the latest approved pipeline version in SageMaker Pipelines. Which approach meets these requirements?
⚠ Common exam trap
Many exam-takers think SageMaker Model Monitor can directly invoke Lambda or update the model registry, but in reality, it only emits events to EventBridge, and the integration requires an intermediate Lambda function to orchestrate the pipeline execution.
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 an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline
It uses an EventBridge rule to listen for SageMaker Model Monitor violation events (e.g., `aws.sagemaker.model-monitoring-violation`), which then triggers a Lambda function that starts the latest approved pipeline version in SageMaker Pipelines. This creates an automated, event-driven retraining pipeline without manual intervention or scheduled polling.
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 a scheduled EventBridge rule to run the pipeline every day
Why it's wrong here
A daily schedule retrains regardless of drift, so it cannot react to Model Monitor violations and ignores the latest approved pipeline version. Scheduled EventBridge rules suit periodic batch retraining where drift detection is unnecessary and a fixed cadence is acceptable.
- ✗
Use SageMaker Model Monitor to update the model registry and trigger a deployment
Why it's wrong here
Model Monitor emits violations to EventBridge; updating the model registry triggers deployment, not retraining, so no pipeline version is selected or executed. Registry approval gates are for promoting models, which would be correct when the goal is controlled rollout rather than automated retraining on drift.
- ✗
Configure SageMaker Model Monitor to directly invoke a Lambda function on violation
Why it's wrong here
A Lambda function invoked on violation can start a pipeline, but the option stops at invocation and never resolves the latest approved pipeline version in SageMaker Pipelines. Direct Lambda invocation fits custom notification or remediation actions, not version-aware pipeline orchestration.
- ✓
Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline
Why this is correct
EventBridge natively consumes SageMaker Model Monitor violation events, and the rule's target Lambda starts the latest approved pipeline version through the SageMaker Pipelines API. This satisfies both constraints: automatic triggering on drift detection and use of the newest approved pipeline version.
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.