MLA-C01 Deployment and Orchestration of ML Workflows Practice Question
A data scientist needs to deploy a single ML model that will serve real-time predictions with low latency (under 10 ms) for a high-traffic web application. The model fits in memory and requires GPU acceleration. Which SageMaker inference option is MOST suitable?
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
✓
Real-time endpoint on ml.g4dn instances
Real-time endpoints on GPU instances (ml.g4dn) provide low latency and GPU acceleration, ideal for high-traffic, latency-sensitive workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Real-time endpoint on ml.m5 instances
Why it's wrong here
ml.m5 instances are CPU-only; they may not achieve sub-10 ms latency for GPU-accelerated models.
- ✗
Batch Transform
Why it's wrong here
Batch Transform is for offline, asynchronous predictions on large datasets, not real-time serving.
- ✓
Real-time endpoint on ml.g4dn instances
Why this is correct
ml.g4dn instances offer GPU acceleration and are designed for low-latency, real-time inference.
- ✗
Serverless Inference
Why it's wrong here
Serverless Inference has a cold start latency that can exceed 10 ms and does not support GPU acceleration.
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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.