MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A machine learning team notices an increase in 5XXError count for a SageMaker endpoint. They want to set up automated remediation. Which THREE actions should they take? (Select THREE)
⚠ Common exam trap
Candidates often confuse enabling detailed monitoring (which only increases metric frequency) with automated remediation, or they mistakenly think manual scaling counts as automated remediation.
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
✓
Add an SNS topic as the alarm action
Option C is correct because a CloudWatch Alarm on the SageMaker endpoint's 5XXError metric is the foundational detection mechanism that turns the rising error count into an actionable state change. Option A is correct because attaching an SNS topic as the alarm action provides the notification/event distribution channel that can fan out the alert to subscribers or trigger downstream automation. Option E is correct because a Lambda function invoked by the alarm (directly or via SNS) performs the automated remediation, such as restarting the endpoint or scaling out its instance count to restore availability. Option B is wrong because manually increasing the instance count is a human intervention, not automated remediation, which is what the team requires. Option D is wrong because enabling detailed monitoring only increases metric granularity (e.g., 1-minute CloudWatch metrics) and does not by itself detect-and-remediate the 5XXError increase.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Add an SNS topic as the alarm action
Why this is correct
An SNS topic as the alarm action provides the notification and integration channel that downstream automation, such as a Lambda function, subscribes to, enabling the automated remediation workflow the team requires when the 5XXError threshold is breached.
- ✗
Increase the endpoint instance count manually
Why it's wrong here
Manually increasing instance count is an operator action, not automated remediation, and it does not respond to 5XXError alarms. It is tempting because scaling out can relieve overload causing 5XX errors, and it would be right for a planned capacity increase, but automation requires alarm-triggered scaling policies instead.
- ✓
Create a CloudWatch Alarm on the 5XXError metric
Why this is correct
A CloudWatch alarm on the 5XXError metric is the detection component: it evaluates the metric against a threshold and changes state, which then triggers the SNS action and remediation logic. Without this alarm, no automated response can fire.
- ✗
Enable detailed monitoring on the endpoint
Why it's wrong here
Detailed monitoring raises CloudWatch metric granularity; it supplies data for alarms but performs no remediation action itself. It is tempting because accurate metrics underpin any automated response, and it would be correct when the goal is finer-grained observability, not the three actions that detect and automatically recover the endpoint.
- ✓
Configure a Lambda function to restart the endpoint or scale out
Why this is correct
A Lambda function provides the compute layer for automated remediation, satisfying the requirement to respond to elevated 5XXError metrics without manual intervention. Invoking it via a CloudWatch alarm lets it restart or scale the endpoint programmatically, directly addressing the increased error count.
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 |
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 665 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
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.