AIF-C01 Fundamentals of Generative AI Practice Question
A company wants to generate product descriptions from a few keywords without managing infrastructure. Which AWS service provides a serverless API for accessing foundation models?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon Bedrock with Amazon SageMaker, assuming SageMaker's JumpStart or hosting capabilities provide the same serverless foundation model access, but SageMaker requires explicit endpoint management and is not a serverless API for pre-built FMs.
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 a single API to access and invoke foundation models (FMs) from leading AI providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. It eliminates the need to manage underlying infrastructure, making it the correct choice for generating product descriptions from keywords without provisioning servers.
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 Lex
Why it's wrong here
Amazon Lex builds conversational chatbots using intents, slots and fulfilment hooks; it does not expose foundation models for text generation. It is tempting because it is serverless and AI-powered, and would be correct for creating a voice or chat interface that answers customer questions about products.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker is a managed platform for building, training, and deploying custom models; it requires provisioning endpoints and infrastructure rather than offering a serverless foundation-model API. It is tempting because it is AWS's flagship machine learning service, but it would be correct for custom model development, not keyword-to-text generation.
- ✓
Amazon Bedrock
Why this is correct
Amazon Bedrock exposes foundation models from multiple providers through a fully managed, serverless API, so no infrastructure is provisioned or scaled by the customer. This matches the stem's serverless requirement for generating product descriptions from keywords.
- ✗
Amazon Comprehend
Why it's wrong here
Amazon Comprehend performs NLP tasks such as sentiment analysis, entity recognition and topic modelling on existing text; it does not host or serve foundation models for generation. It is tempting because it is a fully managed, serverless AI service, and would be the right choice for extracting insights from customer feedback rather than generating product copy.
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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JA
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.