AIF-C01 Fundamentals of Generative AI Practice Question
A startup wants to quickly prototype a generative AI application for summarizing news articles. They have limited ML expertise and want minimal infrastructure management. Which AWS service should they use?
⚠ Common exam trap
AWS often tests the distinction between managed AI services (Bedrock) and infrastructure-heavy services (SageMaker, EC2, Lambda), where candidates mistakenly choose SageMaker for its flexibility or Lambda for its serverless nature, overlooking the specific requirement for minimal ML expertise and infrastructure management.
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 with a foundation model accessed via API.
Amazon Bedrock is the correct choice because it provides pre-trained foundation models from leading AI providers via a simple API, requiring no ML expertise or infrastructure management. The startup can quickly prototype a summarization application by sending news articles to the API and receiving summaries without training or deploying models.
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 with a foundation model accessed via API.
Why this is correct
Amazon Bedrock provides serverless API access to foundation models, eliminating infrastructure management and ML expertise requirements. This satisfies the startup's constraints of minimal infrastructure and limited ML skills, enabling rapid prototyping of news summarisation without training or hosting models.
- ✗
Amazon SageMaker to build and train a custom summarization model.
Why it's wrong here
Building and training a custom summarisation model demands substantial ML expertise and infrastructure management, directly contradicting the startup's constraints. SageMaker is intended for teams that need full control over model training and tuning; it would be the right choice only when bespoke models or proprietary datasets justify that overhead.
- ✗
AWS Lambda with a custom Python script using the Hugging Face Transformers library.
Why it's wrong here
Lambda with Hugging Face Transformers still requires the team to write inference code, manage model artefacts, and handle tokenisation, which assumes ML expertise the startup lacks. Lambda fits event-driven glue code; a fully managed generative AI service is the intended fit for rapid prototyping without model plumbing.
- ✗
Amazon EC2 instance running a pre-trained model from AWS Marketplace.
Why it's wrong here
Running a pre-trained model on EC2 leaves patching, scaling, and inference serving to the startup, so infrastructure management is not minimal. EC2 suits workloads needing OS-level control or custom runtimes; here a managed generative AI service removes that burden entirely.
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Same concept, more angles
1 more way this is tested on AIF-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A developer is building an application that generates product descriptions from images using a multimodal model. Which AWS service provides access to multimodal foundation models?
easy- A.Amazon Rekognition
- B.Amazon Textract
- C.Amazon Comprehend
- ✓ D.Amazon Bedrock
Why D: Amazon Bedrock is a managed service that provides access to a wide range of foundation models (FMs) from leading AI providers, including multimodal models that can process both images and text to generate product descriptions. This makes it the correct choice for building an application that requires multimodal capabilities.
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.