mediumMultiple Select
Required Components for Automated Model Retraining on Performance Degradation
Which THREE components are required to set up automated model retraining in response to performance degradation using Amazon SageMaker? (Select THREE.)
Quick Answer
The answer is an AWS Lambda function that starts a SageMaker training job or pipeline execution, a CloudWatch alarm monitoring a SageMaker Model Monitor quality metric, and a SageMaker Model Monitor schedule that captures inference data and computes metrics. These three components form the core of automated retraining on performance degradation because the Model Monitor schedule continuously tracks model performance, the CloudWatch alarm detects when a metric like accuracy drops below a defined threshold, and the Lambda function triggers the retraining workflow in response to the alarm state. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of event-driven MLOps pipelines, often appearing as a multi-select question where a common trap is choosing a manual approval step or a static endpoint update instead of the automated trigger chain. Remember the sequence: Monitor detects, Alarm alerts, Lambda acts—or simply think “MAL” for Monitor, Alarm, Lambda.
⚠ Common exam trap
Many candidates confuse the monitoring and alerting components (CloudWatch alarm and Model Monitor) with deployment or notification mechanisms, mistakenly selecting manual approval (SNS) or traffic shifting (canary) as part of the automated retraining workflow.
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
✓
A CloudWatch alarm that triggers when a quality metric falls below a threshold.
Option B is correct because a CloudWatch alarm monitoring a model quality metric (e.g., MAE, F1) and firing when it breaches a threshold is the detection mechanism that signals performance degradation and can invoke an action. Option C is correct because SageMaker Model Monitor (specifically a monitoring schedule with a quality baseline) captures inference data from the endpoint and computes the quality metrics that CloudWatch then evaluates. Option D is correct because an AWS Lambda function is the automation glue that, when invoked by the CloudWatch alarm (via SNS or EventBridge), calls the SageMaker API to start a training job or pipeline execution for retraining. Option A is not required because manual approval via an SNS email subscription is a human-in-the-loop step, not a component of automated retraining. Option E is not required because a canary traffic-shift production variant is a deployment strategy for releasing a new model, not a prerequisite for triggering retraining.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
An Amazon SNS topic with a subscription to send a manual approval email.
Why it's wrong here
An SNS topic with manual approval email inserts a human gate; automated retraining must trigger programmatically from a degradation alarm without waiting for a person. It is tempting because approval steps suit high-risk production changes, and it would be correct where governance requires sign-off before a model is promoted.
- ✓
A CloudWatch alarm that triggers when a quality metric falls below a threshold.
Why this is correct
A CloudWatch alarm supplies the degradation trigger the scenario demands: it continuously evaluates a model quality metric against your threshold and fires when performance drops. Without this event source, retraining cannot be automated in response to degradation, only scheduled. It satisfies the requirement for detecting performance decline and initiating the pipeline.
- ✓
A SageMaker Model Monitor schedule to capture inference data and compute quality metrics.
Why this is correct
Model Monitor's schedule captures live inference requests and computes quality metrics against a baseline, producing the degradation signal. This continuous evaluation is what detects drift and feeds the alarm, making it essential to automated retraining.
- ✓
An AWS Lambda function that starts a SageMaker training job or pipeline execution.
Why this is correct
An AWS Lambda function provides the compute layer that translates a degradation alert into action, invoking either a standalone SageMaker training job or a full pipeline execution. This satisfies the automation constraint: retraining triggers without manual intervention, bridging CloudWatch alarms to SageMaker's training APIs.
- ✗
A production variant with a canary traffic shift configuration.
Why it's wrong here
A canary traffic shift governs how a new model version receives inference traffic; it does not supply the monitoring, threshold or retraining trigger that degradation detection requires. It is tempting because canaries are part of safe deployment, and it would be correct when validating a new model version against live traffic before full rollout.
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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Same concept, more angles
2 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer is setting up automated retraining for a model using SageMaker Pipelines. The pipeline should trigger when a data drift alert is received from Model Monitor. Which event source should the engineer use to initiate the pipeline?
hard- ✓ A.Amazon CloudWatch Events (Amazon EventBridge) rule that captures Model Monitor outcome.
- B.AWS Lambda function that polls CloudWatch logs.
- C.S3 event notification on the monitoring output bucket.
- D.SageMaker model monitor webhook.
Why A: Amazon EventBridge (formerly CloudWatch Events) is the native AWS service for reacting to state changes in AWS resources. SageMaker Model Monitor publishes data drift alerts as events to EventBridge, so a rule can be configured to match those specific events and trigger the SageMaker Pipeline execution as a target. This provides a fully managed, event-driven architecture without polling or custom integrations.
Variation 2. A team wants to automatically retrain a model when new labeled data arrives. Which SageMaker feature can orchestrate this workflow?
easy- ✓ A.SageMaker Pipelines
- B.SageMaker Model Monitor
- C.SageMaker Debugger
- D.SageMaker Autopilot
Why A: SageMaker Pipelines is a purpose-built CI/CD service for machine learning that allows you to define, orchestrate, and automate end-to-end ML workflows, including retraining models when new labeled data arrives. You can create a pipeline that triggers on new data events (e.g., via an S3 event notification or a Lambda function) and automatically executes steps such as data processing, training, evaluation, and model registration. This makes it the correct choice for orchestrating an automated retraining workflow.
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.