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, so they cannot provide the GPU acceleration the model requires; latency would also suffer under high traffic. It is tempting because general-purpose real-time endpoints suit low-latency serving when the model runs on CPU and fits comfortably in memory.
- ✗
Batch Transform
Why it's wrong here
Batch Transform processes datasets asynchronously as a job, so it cannot deliver sub-10 ms per-request responses for a live web application. It is tempting because it is the right choice for offline scoring of large stored datasets where latency is irrelevant.
- ✓
Real-time endpoint on ml.g4dn instances
Why this is correct
A real-time endpoint on ml.g4dn instances provides GPU acceleration with persistent, low-latency inference, satisfying the sub-10 ms requirement for a high-traffic web application. Serverless inference lacks GPU support and cold starts, while batch transform cannot serve real-time requests.
- ✗
Serverless Inference
Why it's wrong here
Serverless Inference scales to zero and cold-starts, and its documented latency is seconds, not sub-10 ms; it also lacks GPU support. It is tempting for sporadic, low-traffic workloads where cost matters, but this scenario demands sustained GPU-backed real-time inference.
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