MLS-C01 Modeling Practice Question
A team is deploying a real-time inference endpoint using Amazon SageMaker. The model is a large deep learning model that requires GPU for inference. The endpoint must handle variable traffic patterns with minimal latency. Which deployment strategy should the team use?
⚠ Common exam trap
Many exam-takers assume serverless inference (Option C) is suitable for GPU workloads, but AWS SageMaker Serverless Inference only supports CPU instances, making it incompatible with large deep learning models that require GPU acceleration.
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
✓
Use a SageMaker multi-model endpoint with GPU instance type.
B is correct because SageMaker multi-model endpoints (MMEs) allow multiple models to be hosted on a single GPU-backed endpoint, dynamically loading and unloading models from disk to GPU memory as needed. This reduces cost and cold-start latency compared to single-model endpoints, while still providing GPU acceleration for deep learning inference. MMEs are ideal for variable traffic patterns because they can scale horizontally and share GPU resources efficiently.
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 a single model endpoint with an auto-scaling policy.
Why it's wrong here
Single model endpoint may not efficiently use resources for variable traffic with large models.
- ✓
Use a SageMaker multi-model endpoint with GPU instance type.
Why this is correct
Multi-model endpoints allow hosting multiple models on GPU instances, handling variable traffic efficiently.
- ✗
Deploy a serverless endpoint using SageMaker Serverless Inference.
Why it's wrong here
Serverless Inference currently does not support GPU instances.
- ✗
Use SageMaker Batch Transform to process requests in batches.
Why it's wrong here
Batch Transform is for offline inference, not real-time.
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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.