easyMultiple Choice
MLA-C01 Practice Question: Deploy a machine learning model that was trained…
A company wants to deploy a machine learning model that was trained on-premises using TensorFlow. The model is a TensorFlow SavedModel. The company uses AWS and wants to minimize operational overhead. Which deployment option meets these requirements?
⚠ Common exam trap
AWS often tests the distinction between SageMaker Studio (an IDE) and SageMaker hosting (deployment endpoints), leading candidates to mistakenly select Studio as a deployment option when it is only for development and experimentation.
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
✓
Deploy the model using Amazon SageMaker with a TensorFlow inference container.
Amazon SageMaker provides a fully managed TensorFlow inference container that directly supports TensorFlow SavedModel format, enabling deployment without any custom infrastructure management. This minimizes operational overhead compared to self-managed options like ECS or Lambda, as SageMaker handles scaling, load balancing, and model updates automatically.
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 Amazon ECS using a custom Docker image.
Why it's wrong here
ECS with a custom Docker image requires you to build, patch and scale the container infrastructure yourself, adding operational overhead. It would suit bespoke runtime dependencies, but a TensorFlow SavedModel is served directly by a managed SageMaker endpoint.
- ✗
Deploy the model as an AWS Lambda function with the TensorFlow runtime.
Why it's wrong here
Lambda caps deployment packages and execution duration, so a TensorFlow SavedModel with its runtime dependencies cannot fit or serve sustained inference. It is tempting because Lambda suits small, event-driven functions, and would be correct for lightweight preprocessing or a tiny scikit-learn model rather than a full SavedModel.
- ✗
Deploy the model using Amazon SageMaker Studio.
Why it's wrong here
SageMaker Studio is an integrated development environment for building and training models, not a managed hosting endpoint for an existing SavedModel. It would suit experimentation, but deploying the artefact with minimal operational overhead calls for SageMaker hosting services.
- ✓
Deploy the model using Amazon SageMaker with a TensorFlow inference container.
Why this is correct
SageMaker's TensorFlow inference container natively loads TensorFlow SavedModel artefacts, so the on-premises-trained model deploys without custom serving code or infrastructure management. This satisfies the minimal-operational-overhead constraint, unlike self-managed options such as EC2 or ECS that require patching and scaling work.
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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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.