A startup wants to build a generative AI application that combines a language model with their own business data. They need to test different prompts, ground the model with their data, and deploy the application as a scalable endpoint. Which combination of Azure AI Foundry features should they use?

Content Safety and Azure AI Vision

Prompt playground, prompt flow, and model catalog

Model catalog and Azure Machine Learning pipelines

Azure AI Search and Azure OpenAI Service only

What is the primary purpose of Azure AI Foundry in the context of generative AI workloads?

To provide a unified platform for building, evaluating, and deploying generative AI solutions

To offer a specialized database service optimized for storing vector embeddings

To act as a standalone chatbot service that requires no additional configuration

To serve as a dedicated compute service for training large language models from scratch

A data science team uses Azure AI Foundry to develop a generative AI application. They need to compare several foundation models, test prompts, and then deploy the best model to an endpoint. Which set of Azure AI Foundry features directly supports this workflow?

Data labeling, automated ML, and pipelines

Content Safety, Speech, and Vision

Model catalog, prompt flow, and deployments

Azure DevOps, GitHub Actions, and ARM templates

An organization wants to standardize how different teams access AI models. They need a central place where models are organized by task, with versioning and deployment options, so teams can reuse approved models instead of creating separate deployments. Which capability of the Azure AI Foundry model catalog addresses this need?

Centralized model discovery and management

Microsoft Certified:Azure AI Fundamentals

Domain 5Objective 2

Identify Generative AI Services and Capabilities in Microsoft Azure AI-900 Practice Questions (Page 4)

Part of the Describe features of generative AI workloads on Azure domain, which accounts for 20–25% of the AI-900 exam. Microsoft does not publish an official question count, but from its 60-minute exam (~25–40 total, ~5–10 in this domain), expect 3–5 from this objective — we provide 51 practice questions to prepare you well beyond it. (estimate)

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51questions here

11free pages

9concepts

20–25%of the exam

Questions 16–20

16application · medium
A data science team needs to find a suitable model for a text-generation task. They want to filter models by license, compare performance metrics, and deploy a selected model to an endpoint without writing custom deployment code. Which Azure AI Foundry feature should they use?
Select an answer first
17application · medium
A startup wants to build a generative AI application that combines a language model with their own business data. They need to test different prompts, ground the model with their data, and deploy the application as a scalable endpoint. Which combination of Azure AI Foundry features should they use?
Select an answer first
18foundation · easy
What is the primary purpose of Azure AI Foundry in the context of generative AI workloads?
Select an answer first
19application · medium
A data science team uses Azure AI Foundry to develop a generative AI application. They need to compare several foundation models, test prompts, and then deploy the best model to an endpoint. Which set of Azure AI Foundry features directly supports this workflow?
Select an answer first
20application · medium
An organization wants to standardize how different teams access AI models. They need a central place where models are organized by task, with versioning and deployment options, so teams can reuse approved models instead of creating separate deployments. Which capability of the Azure AI Foundry model catalog addresses this need?
Select an answer first

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