MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team needs to automatically retrain a model when concept drift is detected in the deployed endpoint's predictions. Which TWO steps should they take? (Choose TWO.)
⚠ Common exam trap
MLA-C01 often tests the confusion between data drift (input distribution shift, detected by Data Quality Monitor) and concept drift (prediction quality degradation, detected by Model Quality Monitor) — candidates who pick Data Quality Monitor miss the 'predictions' wording in the question.
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 a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Option B is correct because a CloudWatch alarm on a model quality metric such as accuracy can detect when the deployed model's performance degrades, and its alarm action can invoke a Lambda function that programmatically starts a SageMaker retraining job, giving the automatic, event-driven retraining the team requires. Option C is correct because SageMaker Model Monitor's Model Quality Monitor computes prediction quality metrics (accuracy, precision, recall, etc.) by comparing captured endpoint predictions against ground truth labels, which is exactly the mechanism needed to detect concept drift in the model's predictions. Option A is not appropriate because a fixed EventBridge schedule retrains regardless of whether drift actually occurred, which is time-based rather than drift-triggered. Option D is incorrect because the Data Quality Monitor detects input/data drift (changes in the feature distribution), not concept drift in prediction quality. Option E is incorrect because SageMaker Clarify is used for bias detection and explainability, not for monitoring concept drift in model predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Schedule retraining with Amazon EventBridge on a fixed schedule
Why it's wrong here
Fixed scheduling retrains at arbitrary intervals regardless of drift, so it cannot react to detected concept drift. EventBridge scheduling suits routine, time-based retraining cadences, such as nightly refreshes, where drift detection plays no triggering role.
- ✓
Create a CloudWatch alarm on a model quality metric (e.g., accuracy) and trigger a Lambda function to start a retraining job
Why this is correct
A CloudWatch alarm on a model quality metric detects concept drift by monitoring live prediction accuracy against ground truth. Alarm state-change events invoke Lambda, which programmatically starts a SageMaker retraining job, satisfying the automatic retraining requirement without manual intervention.
- ✓
Set up SageMaker Model Monitor - Model Quality Monitor to compute prediction quality metrics against ground truth
Why this is correct
SageMaker Model Monitor's Model Quality Monitor compares live predictions against ground truth labels, computing metrics like accuracy and F1. This directly satisfies the stem's requirement to detect concept drift, since degrading quality metrics signal that the relationship between features and target has shifted, triggering the automated retraining workflow.
- ✗
Configure SageMaker Model Monitor - Data Quality Monitor to detect input drift
Why it's wrong here
The Data Quality Monitor compares incoming request data against a baseline to detect input drift; concept drift concerns the relationship between inputs and predictions, which requires Model Quality Monitor with ground-truth labels. It is tempting because both monitors detect drift, but only one targets prediction accuracy degradation.
- ✗
Use SageMaker Clarify to monitor bias drift
Why it's wrong here
Clarify monitors bias metrics such as disparate impact, not concept drift in predictions; it cannot detect the drift this scenario requires. Bias monitoring is the right choice when regulatory fairness auditing of model outputs is the goal.
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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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.