This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance and security. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
AWS CloudWatch
Why wrong: CloudWatch monitors infrastructure metrics (CPU, memory), not model prediction quality.
Amazon SageMaker Model Monitor
SageMaker Model Monitor tracks model quality metrics and can trigger retraining.
Amazon Inspector
Why wrong: Inspector is for security assessment, not model monitoring.
AWS Config
Why wrong: Config tracks resource configuration changes, not model performance.
Answer choices
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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 (CPU, memory), not model prediction quality.
Amazon SageMaker Model Monitor
Why this is correct
SageMaker Model Monitor tracks model quality metrics and can trigger retraining.
Related concept
Read the scenario before looking for a memorised answer.
Amazon Inspector
Why it's wrong here
Inspector is for security assessment, not model monitoring.
AWS Config
Why it's wrong here
Config tracks resource configuration changes, not model performance.
Common exam traps
The trap here is that candidates often 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.
Detailed technical explanation
SageMaker Model Monitor works by capturing inference requests and responses from the endpoint, storing them in Amazon S3, and running scheduled monitoring jobs that compare live data statistics (e.g., mean, variance, distribution) against a baseline computed from training data using statistical tests like the Kolmogorov-Smirnov test or L-infinity distance for feature attribution drift. When violations exceed a defined threshold, it publishes metrics to CloudWatch and can invoke an SNS topic to trigger an AWS Lambda function that starts a retraining pipeline, enabling automated model lifecycle management without manual intervention.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
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FAQ
ML Solution Monitoring, Maintenance and Security — This question tests ML Solution Monitoring, Maintenance and Security — Read the scenario before looking for a memorised answer..
The correct answer is: 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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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.
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