- A
Use a scheduled EventBridge rule to run the pipeline every day
Why wrong: Scheduled retraining does not respond to drift events; it runs on a fixed schedule regardless of drift.
- B
Use SageMaker Model Monitor to update the model registry and trigger a deployment
Why wrong: Model Monitor does not update the model registry or trigger deployments; it only monitors and outputs violations.
- C
Configure SageMaker Model Monitor to directly invoke a Lambda function on violation
Why wrong: SageMaker Model Monitor does not directly invoke Lambda; it publishes violations to CloudWatch, which can trigger an alarm or event.
- D
Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline
Model Monitor violations are captured as CloudWatch events; EventBridge can route those events to a Lambda function that starts the retraining pipeline.
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 wants to implement a retraining pipeline that automatically triggers when SageMaker Model Monitor detects data drift. The retraining job should use the latest approved pipeline version in SageMaker Pipelines. Which approach meets these requirements?
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 an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline
Option D is correct because it uses an EventBridge rule to listen for SageMaker Model Monitor violation events (e.g., `aws.sagemaker.model-monitoring-violation`), which then triggers a Lambda function that starts the latest approved pipeline version in SageMaker Pipelines. This creates an automated, event-driven retraining pipeline without manual intervention or scheduled polling.
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.
- ✗
Use a scheduled EventBridge rule to run the pipeline every day
Why it's wrong here
Scheduled retraining does not respond to drift events; it runs on a fixed schedule regardless of drift.
- ✗
Use SageMaker Model Monitor to update the model registry and trigger a deployment
Why it's wrong here
Model Monitor does not update the model registry or trigger deployments; it only monitors and outputs violations.
- ✗
Configure SageMaker Model Monitor to directly invoke a Lambda function on violation
Why it's wrong here
SageMaker Model Monitor does not directly invoke Lambda; it publishes violations to CloudWatch, which can trigger an alarm or event.
- ✓
Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline
Why this is correct
Model Monitor violations are captured as CloudWatch events; EventBridge can route those events to a Lambda function that starts the retraining pipeline.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think SageMaker Model Monitor can directly invoke Lambda or update the model registry, but in reality, it only emits events to EventBridge, and the integration requires an intermediate Lambda function to orchestrate the pipeline execution.
Trap categories for this question
Command / output trap
Model Monitor does not update the model registry or trigger deployments; it only monitors and outputs violations.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor emits violation events to Amazon EventBridge under the `aws.sagemaker` source with detail types like `SageMaker Model Monitor Violation`. The Lambda function can use the AWS SDK to call `sagemaker.start_pipeline_execution` with the latest approved pipeline version ARN, which is typically stored in the model registry or a parameter store. This event-driven architecture ensures retraining only occurs when drift is detected, optimizing cost and compute resources.
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
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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: Create an EventBridge rule that listens for SageMaker Model Monitor violation events and triggers a Lambda function that starts the pipeline — Option D is correct because it uses an EventBridge rule to listen for SageMaker Model Monitor violation events (e.g., `aws.sagemaker.model-monitoring-violation`), which then triggers a Lambda function that starts the latest approved pipeline version in SageMaker Pipelines. This creates an automated, event-driven retraining pipeline without manual intervention or scheduled polling.
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
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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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