AIF-C01 Applications of Foundation Models Practice Question
Which AWS service provides a serverless API for accessing foundation models with per-token pricing?
⚠ Common exam trap
A common mix-up: candidates confuse Amazon API Gateway (a serverless API front-end) with Bedrock's serverless model inference API, or mistakenly think AWS Lambda provides built-in FM access, when in fact Lambda is just compute and requires explicit integration with a model service.
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 service that provides a serverless API for accessing foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI. It offers per-token pricing, meaning you pay only for the number of tokens processed in both input and output, with no upfront commitments or infrastructure management required.
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 Bedrock
Why this is correct
Amazon Bedrock offers a serverless, API-driven way to invoke foundation models from multiple providers, with usage billed per input and output token. That matches the stem's serverless API and per-token pricing constraints without managing infrastructure.
- ✗
Amazon API Gateway
Why it's wrong here
Amazon API Gateway is a serverless front door for routing and throttling your own APIs; it hosts no foundation models and bills per request, not per token. It would be correct for exposing Lambda or HTTP backends as managed REST or WebSocket APIs.
- ✗
AWS Lambda
Why it's wrong here
Lambda executes code but provides no model inference endpoint or per-token billing; you would have to call Amazon Bedrock from within it. It is tempting because Lambda is the standard serverless compute choice, and it would be correct for hosting custom logic that invokes a foundation model.
- ✗
Amazon SageMaker
Why it's wrong here
Amazon SageMaker is a platform for building, training and hosting your own models on provisioned instances, not a serverless per-token API for foundation models. It would be correct for custom training pipelines and managed endpoints with instance-based billing.
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.