Question 1,732 of 1,672
MLS-C01 Modeling Practice Question
A machine learning engineer is deploying a model using Amazon SageMaker. The model requires preprocessing steps (e.g., scaling, encoding) that were applied during training. Which TWO options can ensure the same preprocessing is applied at inference?
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 Scikit-learn pipeline that includes preprocessing and the model, then deploy it.
Options D and E are correct. A Scikit-learn pipeline bundles preprocessing and the model into a single object, ensuring consistent preprocessing during training and inference. SageMaker Inference Pipeline chains a preprocessing container with the model container, allowing separate preprocessing steps to be applied consistently at inference time. Option A is wrong because using a Lambda function can introduce inconsistencies if not carefully managed, and it adds latency. Option B is wrong because a separate preprocessing endpoint adds complexity and may not guarantee identical preprocessing logic. Option C is wrong because retraining the model per inference request is impractical and computationally expensive.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Implement preprocessing as an AWS Lambda function invoked before inference.
Why it's wrong here
Lambda may have different library versions or scaling issues, leading to inconsistencies.
- ✗
Deploy a separate preprocessing endpoint and call it before the model endpoint.
Why it's wrong here
This adds latency and network overhead, and still may have consistency issues.
- ✗
Retrain the model in each inference request with the preprocessing applied.
Why it's wrong here
Retraining per request is computationally infeasible and not how inference works.
- ✓
Create a Scikit-learn pipeline that includes preprocessing and the model, then deploy it.
Why this is correct
The pipeline ensures consistent transformation during training and inference.
- ✓
Use SageMaker Inference Pipeline to chain a preprocessing container with the model container.
Why this is correct
Inference Pipelines ensure the same preprocessing steps are executed in a serial fashion.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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Last reviewed: Jun 20, 2026
This MLS-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 MLS-C01 exam.
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