MLS-C01 Practice Question: Machine Learning Implementation and Operations
A company is using Amazon SageMaker to host a real-time inference endpoint for a natural language processing model. The endpoint is configured with an ml.m5.large instance. After deployment, the company observes that the inference latency is higher than expected, and the endpoint is experiencing CPU utilization near 100% during peak hours. The model is a PyTorch model that uses a transformer architecture. The company wants to reduce latency without increasing cost significantly. Which approach should the company take?
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
✓
Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.
The issue is high CPU utilization causing latency for a PyTorch transformer model. GPU instances like ml.g4dn.xlarge can significantly accelerate inference through parallel processing, reducing latency. Option D is correct. Elastic Inference (C) may provide some acceleration but is less effective for transformer models and adds complexity. Auto Scaling (A) helps with traffic spikes but does not reduce per-request latency. Batch Transform (B) is for offline inference, not real-time.
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 the endpoint with Auto Scaling to add more instances during peak hours.
Why it's wrong here
Wrong: Auto Scaling handles traffic but does not reduce per-request CPU bottleneck.
- ✗
Switch to batch transform for inference.
Why it's wrong here
Wrong: Batch transform is not real-time; it does not meet low-latency requirements.
- ✗
Attach an Elastic Inference accelerator to the existing instance.
Why it's wrong here
Wrong: Elastic Inference may not provide enough acceleration for transformer models and adds latency overhead.
- ✓
Change the endpoint instance type to ml.g4dn.xlarge to use GPU acceleration.
Why this is correct
Correct: GPU instances accelerate transformer inference, reducing latency.
Go deeper
Related to this question
About these practice questions
This MLS-C01 question is part of Courseiva's 1,672-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam 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 MLS-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 MLS-C01 exam.