easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A company has deployed a SageMaker real-time…
A company has deployed a SageMaker real-time endpoint for a model that predicts customer churn. The endpoint uses a single ml.m5.large instance. After deployment, the team notices that during peak hours, the endpoint returns 5xx errors for about 20% of requests. The endpoint has not been configured with any scaling policy. The team needs to resolve this issue with minimal cost increase. Which solution should the team implement?
⚠ Common exam trap
It's easy for candidates to confuse 'scaling up' (increasing instance size) with 'scaling out' (adding more instances), and overlook that Auto Scaling with a target tracking policy is the most cost-effective way to handle variable traffic, as it matches capacity to demand in real time.
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
✓
Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.
Enabling Auto Scaling with a target tracking policy based on the average InvocationsPerInstance metric dynamically adjusts the number of instances in response to traffic spikes, preventing 5xx errors during peak hours without over-provisioning. This approach minimizes cost by scaling only when needed, unlike manual instance upgrades or batch transforms that either increase baseline cost or introduce latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Deploy the model to a multi-model endpoint to reduce resource utilization.
Why it's wrong here
Multi-model endpoints share resources among models but do not automatically scale to handle traffic spikes.
- ✓
Enable Auto Scaling for the endpoint with a target tracking policy based on the average InvocationsPerInstance metric.
Why this is correct
Auto Scaling adds instances only when needed, minimizing cost while handling peak load.
- ✗
Increase the instance type to ml.m5.xlarge to handle more concurrent requests.
Why it's wrong here
This increases cost even during low traffic, not minimal cost increase.
- ✗
Use SageMaker batch transform instead of real-time inference to process peak traffic asynchronously.
Why it's wrong here
Batch transform is not suitable for real-time applications requiring low latency.
Visual reference
Go deeper
Related to this question
About these practice questions
One of 835 original MLA-C01 practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. 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.