- A
Configure Model Monitor to directly invoke a SageMaker Pipeline when drift is detected
Why wrong: Model Monitor does not directly invoke pipelines; it publishes metrics to CloudWatch.
- B
Configure a SageMaker Processing job to run periodically and check drift
Why wrong: This is an alternative to Model Monitor, not part of the alert/retraining flow.
- C
Set up an SNS subscription that triggers a Lambda function to start the SageMaker Pipeline
Lambda function subscribed to SNS can start the pipeline programmatically.
- D
Create a CloudWatch Alarm on the data quality violation metric that publishes to an SNS topic
CloudWatch Alarm triggered by violation metric sends alarm state to SNS.
- E
Create an EventBridge rule that triggers on Model Monitor drift events to start the pipeline
Why wrong: Model Monitor does not emit events to EventBridge.
MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
This MLA-C01 practice question tests your understanding of ml solution monitoring, maintenance, and security. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 company uses SageMaker Model Monitor to detect data drift. They want to receive alerts when drift is detected and automatically trigger a retraining pipeline. Which TWO steps should they implement? (Select TWO.)
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
Set up an SNS subscription that triggers a Lambda function to start the SageMaker Pipeline
Option C is correct because Amazon SNS can be used to publish a notification when Model Monitor detects data drift, and a Lambda function subscribed to that SNS topic can invoke the SageMaker Pipeline to trigger retraining. This decouples the monitoring from the pipeline execution, allowing for flexible, event-driven automation. Option D is correct because Model Monitor emits CloudWatch metrics for data quality violations, and you can create a CloudWatch Alarm on those metrics to publish to an SNS topic, which can then trigger a retraining pipeline via Lambda or other integrations.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure Model Monitor to directly invoke a SageMaker Pipeline when drift is detected
Why it's wrong here
Model Monitor does not directly invoke pipelines; it publishes metrics to CloudWatch.
- ✗
Configure a SageMaker Processing job to run periodically and check drift
Why it's wrong here
This is an alternative to Model Monitor, not part of the alert/retraining flow.
- ✓
Set up an SNS subscription that triggers a Lambda function to start the SageMaker Pipeline
Why this is correct
Lambda function subscribed to SNS can start the pipeline programmatically.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Create a CloudWatch Alarm on the data quality violation metric that publishes to an SNS topic
Why this is correct
CloudWatch Alarm triggered by violation metric sends alarm state to SNS.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Create an EventBridge rule that triggers on Model Monitor drift events to start the pipeline
Why it's wrong here
Model Monitor does not emit events to EventBridge.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think Model Monitor can directly trigger pipelines or emit EventBridge events, but in reality it relies on CloudWatch metrics and SNS for downstream automation.
Detailed technical explanation
How to think about this question
Model Monitor uses a built-in baseline and constraint file to compare inference data against, and it publishes violation metrics to CloudWatch under the namespace /aws/sagemaker/ModelMonitor. The CloudWatch Alarm on the 'DataQualityViolations' metric can be set with a threshold of >0 to trigger an SNS topic, which then invokes a Lambda function that calls the SageMaker Pipeline's start execution API. This pattern ensures retraining is triggered only when drift is actually detected, avoiding unnecessary compute costs.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
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.
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 |
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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ML Solution Monitoring, Maintenance, and Security — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
ML Solution Monitoring, Maintenance, and Security — This question tests ML Solution Monitoring, Maintenance, and Security — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Set up an SNS subscription that triggers a Lambda function to start the SageMaker Pipeline — Option C is correct because Amazon SNS can be used to publish a notification when Model Monitor detects data drift, and a Lambda function subscribed to that SNS topic can invoke the SageMaker Pipeline to trigger retraining. This decouples the monitoring from the pipeline execution, allowing for flexible, event-driven automation. Option D is correct because Model Monitor emits CloudWatch metrics for data quality violations, and you can create a CloudWatch Alarm on those metrics to publish to an SNS topic, which can then trigger a retraining pipeline via Lambda or other integrations.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jul 4, 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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