AIF-C01 Fundamentals of Generative AI Practice Question
A startup wants to generate product descriptions from a few keywords using a foundation model. They need a fully managed serverless solution that requires no infrastructure setup. Which AWS service should they use?
⚠ Common exam trap
Candidates often confuse Amazon SageMaker's managed ML capabilities with a serverless generative AI service, overlooking that SageMaker requires explicit infrastructure setup for model hosting, while Bedrock is purpose-built for serverless access to foundation models.
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
✓
Amazon Bedrock
Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via a simple API, making it ideal for generating product descriptions from keywords without any infrastructure management. It directly supports generative AI tasks like text generation, unlike other AWS services that focus on different ML or NLP capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker provides model training and hosting but requires configuring endpoints, instances and scaling, so it is not serverless with zero infrastructure setup. It is tempting because it is AWS's flagship machine learning platform, yet the stem demands managed inference without provisioning or capacity management.
- ✗
Amazon Comprehend
Why it's wrong here
Comprehend performs natural language analysis such as sentiment, entities and key phrases; it does not generate new text from prompts. It is tempting as a managed AI service requiring no infrastructure, but its purpose is extracting insight from existing text, not producing product descriptions.
- ✗
AWS Lambda
Why it's wrong here
Lambda executes code but hosts no foundation model; generating descriptions requires invoking a model endpoint, which Lambda would only orchestrate. It is tempting as the canonical serverless compute service, yet the requirement is managed model inference, not general-purpose function execution.
- ✓
Amazon Bedrock
Why this is correct
Amazon Bedrock provides serverless access to foundation models through a single API, so the startup generates product descriptions from keywords without provisioning or managing any infrastructure. It satisfies the stem's fully managed, no-setup constraint directly, unlike self-hosted alternatives requiring instance or endpoint management.
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 AIF-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 AIF-C01 exam.