AIF-C01 Applications of Foundation Models Practice Question
A startup wants to add image generation to its design tool. The team does not want to manage GPU infrastructure, train models, or host endpoints, and they want to call a managed API for a text-to-image foundation model available in Amazon Bedrock. Which action should they take?
⚠ Common exam trap
The trap here is matching the word image to Amazon Rekognition, which analyzes existing images rather than generating new ones from text.
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
✓
Use an Amazon Bedrock image generation model through the InvokeModel API with a text prompt.
Amazon Bedrock exposes image generation foundation models such as Amazon Titan Image Generator as managed, serverless APIs invoked with a text prompt. This delivers text-to-image capability without GPU provisioning, training, or endpoint management. SageMaker self-hosting adds operational work, while Rekognition analyzes images and Polly synthesizes speech, so neither can generate design images from text.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use an Amazon Bedrock image generation model through the InvokeModel API with a text prompt.
Why this is correct
Amazon Bedrock provides serverless access to image generation foundation models, including Amazon Titan Image Generator, through the InvokeModel API. The startup sends a text prompt and receives generated images without provisioning GPUs or managing endpoints. This matches the requirement for a managed text-to-image API with no infrastructure ownership.
- ✗
Use Amazon Polly to convert text prompts into images for the design tool.
Why it's wrong here
Amazon Polly is a text-to-speech service that turns written text into lifelike audio. It produces sound, not images, so it cannot generate design assets. The startup needs a generative image model, and Polly addresses an entirely different modality, making this option incorrect for the stated goal.
- ✗
Use Amazon Rekognition to synthesize new design images from text descriptions.
Why it's wrong here
Amazon Rekognition performs image and video analysis such as label detection, moderation, and face comparison. It does not generate new images from text prompts, so it cannot fulfill a text-to-image design feature. Selecting it would be a fundamental mismatch between the service's purpose and the requirement.
- ✗
Deploy an open-source diffusion model on Amazon SageMaker endpoints and manage scaling themselves.
Why it's wrong here
Hosting a diffusion model on SageMaker requires provisioning instances, configuring autoscaling, and managing the endpoint lifecycle, which contradicts the goal of avoiding infrastructure management. It also duplicates capabilities already offered as a managed API in Amazon Bedrock. The startup would take on operational burden it explicitly wants to avoid.
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 and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Amazon Web Services exam blueprint
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.