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MLA-C01 Practice Question: A data science team has trained a PyTorch model…
A data science team has trained a PyTorch model using Amazon SageMaker and wants to deploy it with a custom inference container that includes a pre-processing step. The team needs to minimize latency and ensure the pre-processing runs only once per request. Which SageMaker real-time inference option should they use?
⚠ Common exam trap
AWS often tests the distinction between a single-container approach (Option C) and a multi-container pipeline (Option D), where candidates mistakenly think a single custom container is simpler and sufficient, but the pipeline is required to guarantee that pre-processing runs exactly once per request and to allow independent scaling or updates of the pre-processing logic.
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 SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
A SageMaker inference pipeline allows you to chain two containers in a single endpoint, where the first container handles pre-processing and the second runs inference. This ensures that pre-processing runs exactly once per request, minimizing latency by avoiding redundant processing and keeping the request within the same HTTP connection.
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 the model on a multi-model endpoint and include pre-processing in the model code.
Why it's wrong here
Multi-model endpoints host multiple models on the same instance but do not support chaining containers for pre-processing.
- ✗
Use a batch transform job with a pre-processing script.
Why it's wrong here
Batch transform is for asynchronous batch predictions, not real-time inference.
- ✗
Package pre-processing and inference in a single container with a custom entry point.
Why it's wrong here
While possible, this doesn't leverage SageMaker's pipeline optimization and may be harder to maintain.
- ✓
Create a SageMaker inference pipeline with two containers: one for pre-processing and one for inference.
Why this is correct
An inference pipeline chains containers sequentially, allowing pre-processing to run once per request with low latency.
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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.