hardMultiple ChoiceObjective-mapped
MLA-C01 Auto-scaling Practice Question
A data science team at a financial services company is deploying a real-time fraud detection model using Amazon SageMaker. The model is a gradient boosting classifier trained on historical transaction data. The model is deployed to a SageMaker endpoint with an ML.M5.LARGE instance for real-time inference. After deployment, the team observes that the endpoint's latency spikes to over 2 seconds during peak hours (10:00-12:00 and 14:00-16:00), causing timeouts for client applications. The average latency during off-peak hours is 200 ms. The team has enabled auto-scaling with a target average CPU utilization of 70%, but the endpoint still experiences high latency during peak hours. The instance count never scales beyond 2 instances during peaks. The model size is 500 MB, and each request includes 200 features. The team needs to reduce latency to under 500 ms at the 99th percentile during peak hours without increasing costs beyond the current budget. Which course of action should the team take?
⚠ Common exam trap
The trap is that candidates might assume GPU acceleration is the standard fix for high latency, but gradient boosting models are CPU-bound. Horizontal scaling (more instances) is the appropriate and cost-effective solution.
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
✓
Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.
The root cause of high latency during peak hours is insufficient compute capacity. By increasing the auto-scaling maximum instance count to 10 and lowering the target CPU utilization to 50%, the endpoint will scale out more aggressively during peak traffic, distributing the inference load across more instances. This reduces per-instance CPU utilization and latency without resorting to more expensive GPU instances. The current budget likely supports up to 10 instances, so costs remain within budget. Option C is incorrect because gradient boosting inference is CPU-bound and does not benefit significantly from GPU acceleration; GPU instances are also more expensive, potentially increasing costs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Configure SageMaker batch transform for the real-time endpoint to process requests asynchronously.
Why it's wrong here
Incorrect. SageMaker batch transform is designed for offline batch processing, not for real-time inference. Using it for a real-time endpoint would not reduce latency and would break the real-time requirement.
- ✓
Increase the auto-scaling maximum instance count to 10 and set target CPU utilization to 50%.
Why this is correct
Correct. Increasing the maximum instance count and lowering the CPU utilization target allows the endpoint to scale out to more instances during peak hours, distributing the workload and reducing latency. This addresses the compute bottleneck without requiring GPU instances or incurring extra costs if the budget accommodates the higher maximum.
- ✗
Switch the endpoint instance type to a GPU instance such as ml.g4dn.xlarge to accelerate inference.
Why it's wrong here
Incorrect. Gradient boosting models are typically CPU-bound and do not achieve significant speedups on GPU instances. GPU instances are also more expensive per hour, which could increase costs beyond the current budget.
- ✗
Enable data compression on the endpoint to reduce payload size and network latency.
Why it's wrong here
Incorrect. Data compression reduces payload size and network latency, but the primary bottleneck during peak hours is compute capacity (CPU), not network. Compressing data would not sufficiently reduce inference latency to under 500 ms at the 99th percentile.
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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.