MLS-C01 Modeling Practice Question
A company is deploying a real-time inference endpoint with SageMaker. The model is a large neural network that requires GPU acceleration. Which TWO configurations must be set?
⚠ Common exam trap
It's easy for candidates to confuse the required configurations for deploying a real-time endpoint with those for training or batch processing, mistakenly selecting Batch Transform or Training Container Image instead of recognizing that the instance type with GPU and the SageMaker model definition are the two essential components.
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
✓
Instance type with GPU
Deploying a real-time inference endpoint with a large neural network that requires GPU acceleration necessitates selecting an instance type with a GPU, such as the ml.p3 or ml.g4dn series, to provide the parallel processing power needed for low-latency inference. Without a GPU instance, the model would fall back to CPU, leading to unacceptable inference times for large neural networks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Instance type with GPU
Why this is correct
Required for GPU inference.
- ✓
Create a SageMaker model with the inference code and model artifacts
Why this is correct
Required to deploy endpoint.
- ✗
Batch transform job
Why it's wrong here
For offline inference.
- ✗
Production variant
Why it's wrong here
Part of endpoint configuration, but not a separate configuration.
- ✗
Training container image
Why it's wrong here
Inference container is different.
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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.