MLA-C01 Practice Question: ML Solution Monitoring, Maintenance, and Security
A data scientist notices that a SageMaker endpoint is returning HTTP 5XX errors under high load. The endpoint uses a single ml.m5.large instance. The team wants to reduce these errors without changing the instance type. What is the most cost-effective step?
⚠ Common exam trap
Many candidates think increasing the timeout (Option A) or using batch transform (Option B) can solve real-time load issues, but these options do not address the fundamental need for horizontal scaling under high concurrency.
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
✓
Configure auto-scaling for the endpoint with a target tracking policy
Configuring auto-scaling with a target tracking policy allows the endpoint to dynamically add more instances under high load, distributing the traffic and reducing HTTP 5XX errors. Since the team cannot change the instance type, scaling out is the most cost-effective way to handle increased demand, as it only adds capacity when needed and avoids over-provisioning.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the endpoint's invocation timeout to 120 seconds
Why it's wrong here
Timeout does not address capacity; requests may still fail due to overload.
- ✗
Deploy the model on a SageMaker batch transform job
Why it's wrong here
Deploying on a batch transform job does not serve a real-time inference endpoint, so it cannot handle the live, synchronous requests that are causing the HTTP 5XX errors under load. This option is tempting because batch transform is designed for offline, asynchronous predictions on large datasets, and it would be the correct choice if the workload were non-real-time and could tolerate queued processing.
- ✓
Configure auto-scaling for the endpoint with a target tracking policy
Why this is correct
Auto-scaling adds instances during high load and removes them when traffic subsides, reducing errors cost-effectively.
- ✗
Create a new endpoint with multiple instances and use weighted routing
Why it's wrong here
Overprovisioning is not cost-effective; auto-scaling is better.
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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.