AIF-C01 Applications of Foundation Models Practice Question
A retail company wants to build an application that uses a foundation model on Amazon Bedrock to answer customer questions about product availability. They need the model to access real-time inventory data from their internal database and perform actions such as reserving an item. Which TWO capabilities should they implement to achieve this? (Choose two.)
⚠ Common exam trap
The trap here is assuming that RAG alone can provide real-time data and actions, when live database access and transactions require agent action groups backed by compute.
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
✓
Define an action group in the agent that maps to an API schema for the inventory and reservation operations.
Amazon Bedrock Agents orchestrate tasks by invoking action groups, which are defined with OpenAPI schemas and backed by Lambda functions. This allows the agent to query real-time inventory and perform reservations. RAG over static documents, temperature changes, and logging do not provide live data access or transactional capabilities, so they cannot satisfy the requirements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Enable model invocation logging to capture inventory queries.
Why it's wrong here
Model invocation logging records requests and responses for audit and troubleshooting. It does not connect the model to a database or allow it to perform actions. While logging is useful for monitoring, it is passive and cannot retrieve live inventory or reserve items, so it does not fulfill the functional needs described.
- ✗
Use Retrieval Augmented Generation with a static document store containing product manuals.
Why it's wrong here
RAG with a static document store retrieves information from pre-indexed documents, not live inventory. Product manuals do not contain real-time stock levels or reservation capabilities. While RAG is useful for knowledge grounding, it cannot satisfy the requirement for current inventory data or transactional actions, so it is not appropriate here.
- ✓
Define an action group in the agent that maps to an API schema for the inventory and reservation operations.
Why this is correct
Action groups in Amazon Bedrock Agents use OpenAPI schemas to describe available operations. By defining an action group with the inventory query and reservation endpoints, the agent knows how and when to call them. This enables the model to perform the required actions through the agent's orchestration, making it a correct implementation step.
- ✓
Use Amazon Bedrock Agents to orchestrate calls to an AWS Lambda function that queries the inventory database.
Why this is correct
Amazon Bedrock Agents can orchestrate multi-step tasks by invoking action groups backed by Lambda functions. The agent interprets the user's request, calls the Lambda to query real-time inventory, and uses the result to formulate a response. This directly enables access to internal data and supports actions like reservations, making it a correct choice for the scenario.
- ✗
Increase the model's temperature to allow more creative problem-solving.
Why it's wrong here
Temperature affects randomness in text generation and does not grant access to external data or actions. Raising it would make responses less predictable, which is undesirable for factual inventory queries. It does not integrate with databases or enable reservations, so it fails to meet the scenario's requirements for real-time data and actions.
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.