easyMultiple Choice
MLA-C01 Practice Question: A data science team deploys a regression model…
A data science team deploys a regression model using Amazon SageMaker. After one week, the model's prediction accuracy drops significantly. The team needs to detect this degradation automatically and trigger retraining. Which AWS service should they use to monitor the model's performance over time and set up alerts?
⚠ Common exam trap
A common mix-up: candidates confuse general-purpose monitoring services like CloudWatch with model-specific monitoring tools, overlooking that SageMaker Model Monitor provides built-in drift detection and retraining triggers tailored for ML models, whereas CloudWatch requires extensive custom scripting to achieve the same functionality.
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
✓
Amazon SageMaker Model Monitor
Amazon SageMaker Model Monitor is the correct choice because it is purpose-built to continuously monitor machine learning models deployed on SageMaker endpoints for data drift, feature attribution drift, and prediction quality degradation. It automatically compares live inference data against a baseline, triggers alerts when performance drops, and can be configured to initiate retraining pipelines via AWS Lambda or Step Functions, directly addressing the need to detect accuracy degradation and trigger 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.
- ✗
AWS CloudWatch
Why it's wrong here
CloudWatch monitors infrastructure metrics such as CPU and latency, but SageMaker Model Monitor publishes prediction-quality metrics to it; CloudWatch alone cannot compute accuracy against ground truth. It is tempting because CloudWatch does host alarms and dashboards, and would be correct for alerting on endpoint latency or invocation errors.
- ✓
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor continuously captures inference data and compares it against a baseline, detecting data drift and model quality degradation. It publishes metrics to CloudWatch, letting the team set alarms that automatically trigger retraining pipelines when accuracy falls below threshold.
- ✗
Amazon Inspector
Why it's wrong here
Amazon Inspector scans EC2 instances, container images and Lambda functions for software vulnerabilities and unintended network exposure, not model prediction accuracy. It is tempting because it does assess deployed artefacts continuously, and it would be correct for finding CVEs in the custom inference container image.
- ✗
AWS Config
Why it's wrong here
AWS Config records resource configuration changes and evaluates compliance rules, not model prediction accuracy, so it cannot detect drift or trigger retraining. It is tempting because Config does track changes over time, and it would be correct for auditing whether an endpoint's configuration drifted from an approved baseline.
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 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.