MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A company wants to automatically trigger a retraining pipeline when concept drift is detected in their deployed model. Which combination of services should they use?
⚠ Common exam trap
A common exam trap is the distinction between monitoring services (Model Monitor for drift vs. Clarify for bias) and the correct event chain (Model Monitor → CloudWatch → SNS → Lambda) versus incomplete chains like direct Lambda invocation or using the wrong service for 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
✓
SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda
SageMaker Model Monitor detects concept drift by analyzing model predictions against a baseline, then publishes metrics to CloudWatch. A CloudWatch Alarm triggers when drift exceeds a threshold, sending a notification via SNS to invoke a Lambda function, which starts the retraining pipeline. This end-to-end integration ensures automated, event-driven retraining without manual intervention.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
SageMaker Model Monitor → Lambda
Why it's wrong here
SageMaker Model Monitor detects drift and emits metrics to CloudWatch, but it does not itself invoke Lambda; the missing link is an EventBridge rule matching those metrics. Model Monitor is correct when you only need to observe and alert on drift, not act on it.
- ✗
CloudWatch Events → SageMaker Training Job
Why it's wrong here
CloudWatch Events can trigger a training job, but it reacts to scheduled or metric events, not to SageMaker Model Monitor's drift findings; without Model Monitor there is no concept-drift signal. It suits retraining on a fixed schedule or on infrastructure events.
- ✓
SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda
Why this is correct
SageMaker Model Monitor detects drift and emits metrics; CloudWatch alarms on those thresholds, SNS fans out the notification, and Lambda invokes the retraining pipeline. This chain satisfies the requirement to trigger retraining automatically upon concept drift detection without manual intervention.
- ✗
SageMaker Clarify → SNS → Step Functions
Why it's wrong here
SageMaker Clarify detects bias and explainability issues, not concept drift, so it cannot supply the trigger; SNS and Step Functions only orchestrate once a signal exists. Clarify is the right choice when monitoring for bias in predictions or feature attribution.
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
One of 665 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.