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MLA-C01 Practice Question: A data science team needs to deploy a PyTorch…
A data science team needs to deploy a PyTorch model for real-time inference with low latency. The model requires GPU acceleration. Which SageMaker endpoint configuration should they use?
⚠ Common exam trap
It's easy for candidates to confuse batch transform jobs or serverless endpoints with real-time inference, overlooking the explicit GPU requirement and the need for persistent, low-latency compute resources.
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
✓
Create a real-time endpoint using an ml.p3.2xlarge instance
Real-time SageMaker endpoints with GPU instances like ml.p3.2xlarge are specifically designed for low-latency, synchronous inference with GPU acceleration. PyTorch models requiring GPU must use instance types that support NVIDIA CUDA, and the ml.p3 family provides the necessary GPU compute for real-time predictions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Create a multi-model endpoint using ml.m5.large instances
Why it's wrong here
Multi-model endpoints share a single GPU across multiple models, which introduces contention and unpredictable latency, failing the low-latency real-time requirement. This option is tempting because multi-model endpoints reduce cost by hosting several models on one instance, making them ideal for scenarios with infrequent or variable inference traffic where throughput can be traded for lower infrastructure expense.
- ✗
Create a serverless endpoint with memory set to 6144 MB
Why it's wrong here
Serverless endpoints do not support GPU.
- ✗
Create a batch transform job using an ml.c5.xlarge instance
Why it's wrong here
Batch transform is not real-time and uses CPU.
- ✓
Create a real-time endpoint using an ml.p3.2xlarge instance
Why this is correct
Real-time endpoints support GPU instances for low-latency inference.
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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.