AIF-C01 Fundamentals of Generative AI Practice Question
A startup wants to integrate a generative AI chatbot into their mobile app with minimal latency. Which AWS service is purpose-built for deploying foundation models with low latency and high throughput?
⚠ Common exam trap
Candidates often mistake Amazon SageMaker for the purpose-built service, but SageMaker requires manual infrastructure management for low-latency generative AI inference, whereas Amazon Bedrock is a fully managed, serverless service designed specifically for deploying foundation models with low latency and high throughput.
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 access to foundation models (FMs) from leading AI providers via a serverless API, purpose-built for deploying generative AI applications with low latency and high throughput. It handles the underlying infrastructure, model hosting, and scaling automatically, making it ideal for integrating a generative AI chatbot into a mobile app with minimal latency.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AWS Lambda
Why it's wrong here
Lambda runs short-lived functions with cold starts and execution time limits, so it cannot host a foundation model for continuous low-latency inference. It is the right choice for event-driven glue code, such as calling Amazon Bedrock from an API backend.
- ✗
Amazon SageMaker
Why it's wrong here
SageMaker provides the training and hosting toolchain, but its real-time endpoints are not purpose-built for token streaming at the throughput and latency Amazon Bedrock delivers. It suits custom model training, fine-tuning and full MLOps control, where you manage infrastructure yourself.
- ✓
Amazon Bedrock
Why this is correct
Amazon Bedrock provides serverless access to foundation models through a single API with low-latency, high-throughput inference, meeting the chatbot's responsiveness requirement. It avoids managing infrastructure, unlike self-hosted options such as Amazon SageMaker endpoints or EC2-based deployments.
- ✗
Amazon Transcribe
Why it's wrong here
Transcribe converts speech to text; it performs no generative inference, so it cannot serve a chatbot's foundation model. It would be correct when the app needs automatic speech recognition, for example transcribing voice input before sending text to a model.
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.