- A
SageMaker Model Monitor → Lambda
Why wrong: Missing CloudWatch Alarm and SNS; Lambda cannot directly subscribe to Model Monitor output.
- B
CloudWatch Events → SageMaker Training Job
Why wrong: This lacks the drift detection step; it would trigger on a schedule, not on drift.
- C
SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda
This is the standard architecture for automated drift-based retraining.
- D
SageMaker Clarify → SNS → Step Functions
Why wrong: SageMaker Clarify is not designed for drift detection; it handles bias and explainability.
Question 751 of 1,000
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 automatically trigger a retraining pipeline when concept drift is detected in their deployed model. Which combination of services should they use?
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
✓
SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda
Option C is correct because SageMaker Model Monitor detects concept drift by analyzing model predictions against a baseline, then publishes metrics to CloudWatch. A CloudWatch Alarm triggers when drift exceeds a threshold, sending a notification via SNS to invoke a Lambda function, which starts the retraining pipeline. This end-to-end integration ensures automated, event-driven retraining without manual intervention.
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.
- ✗
SageMaker Model Monitor → Lambda
Why it's wrong here
Missing CloudWatch Alarm and SNS; Lambda cannot directly subscribe to Model Monitor output.
- ✗
CloudWatch Events → SageMaker Training Job
Why it's wrong here
This lacks the drift detection step; it would trigger on a schedule, not on drift.
- ✓
SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda
Why this is correct
This is the standard architecture for automated drift-based retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
SageMaker Clarify → SNS → Step Functions
Why it's wrong here
SageMaker Clarify is not designed for drift detection; it handles bias and explainability.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Cisco often tests the distinction between monitoring services (Model Monitor for drift vs. Clarify for bias) and the correct event chain (Model Monitor → CloudWatch → SNS → Lambda) versus incomplete chains like direct Lambda invocation or using the wrong service for drift detection.
Trap categories for this question
Command / output trap
Missing CloudWatch Alarm and SNS; Lambda cannot directly subscribe to Model Monitor output.
Detailed technical explanation
How to think about this question
SageMaker Model Monitor uses a baseline statistics and constraints file (generated from training data) to compute drift metrics like distribution distances (e.g., KL divergence, L-infinity norm) for each feature and prediction. These metrics are emitted as CloudWatch metrics, and a CloudWatch Alarm with a threshold (e.g., violation count > 5 in 10 minutes) triggers an SNS topic, which invokes a Lambda function that can call the SageMaker API (e.g., CreateTrainingJob) to start retraining. In practice, this pipeline is critical for production models where data distributions shift over time, such as in fraud detection or recommendation systems, to maintain accuracy without manual checks.
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: SageMaker Model Monitor → CloudWatch Alarm → SNS → Lambda — Option C is correct because SageMaker Model Monitor detects concept drift by analyzing model predictions against a baseline, then publishes metrics to CloudWatch. A CloudWatch Alarm triggers when drift exceeds a threshold, sending a notification via SNS to invoke a Lambda function, which starts the retraining pipeline. This end-to-end integration ensures automated, event-driven retraining without manual intervention.
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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