mediumMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: An MLOps engineer is setting up a SageMaker…
An MLOps engineer is setting up a SageMaker endpoint for a model that performs inference on large images. The model is containerized and expects input in a specific format. The team wants to preprocess the images (resize and normalize) before passing them to the model. What is the most efficient way to implement this?
⚠ Common exam trap
A common mix-up: candidates choose Option C (packaging everything into one container) because it seems simpler, but they overlook the fact that SageMaker Inference Pipelines are specifically designed for this exact use case and provide better modularity, maintainability, and efficiency.
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
✓
Configure SageMaker to use a preprocessing container as the first step of an inference pipeline, followed by the model container.
SageMaker Inference Pipelines allow you to chain multiple containers in a serial fashion, where the output of one container becomes the input of the next. By placing a preprocessing container as the first step, you can resize and normalize large images before passing them to the model container, which keeps the model container focused on inference and avoids unnecessary data transfer or custom code. This is the most efficient and natively supported approach within SageMaker for multi-step inference workflows.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Configure SageMaker to use a preprocessing container as the first step of an inference pipeline, followed by the model container.
Why this is correct
Inference pipeline allows separation of concerns and efficient processing.
- ✗
Use Amazon API Gateway to perform request transformation before forwarding to the endpoint.
Why it's wrong here
API Gateway has payload limits and is not suitable for large image preprocessing.
- ✗
Package the preprocessing logic into the same Docker container as the model.
Why it's wrong here
Coupling preprocessing and model reduces flexibility and reusability.
- ✗
Use a Lambda function as a proxy to preprocess requests before calling the SageMaker endpoint.
Why it's wrong here
Lambda adds latency and cost, and is not efficient for large payloads.
Go deeper
Related to this question
About these practice questions
Courseiva writes every MLA-C01 question from scratch — 835 in total, each with an explanation and a wrong-answer breakdown. None are copied from real exams or dumps. Learn why practice questions differ from exam dumps →
JA
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.