Developing AI Apps and Agents on Azure (AI-103) Practice Test
Build your confidence for Developing AI Apps and Agents on Azure (AI-103). Practice the concepts, understand the answers, and strengthen your knowledge one question at a time.
Try a sample questionExam overview and details
Practice building and managing AI solutions on Azure while you prepare for the Developing AI Apps and Agents on Azure AI-103 Practice Test. Through scenario-based questions, you can strengthen your understanding of planning and managing an Azure AI solution, implementing generative AI and agentic solutions, and working with computer vision, text analysis, and information extraction. Each practice session is a manageable next step toward applying these skills with more confidence.
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Sample Questions
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An organization already uses Azure AI Foundry models but has not set up any Foundry projects. They want to deploy and manage these models through the REST API. Which deployment approach is most suitable?
A company has deployed an AI agent to handle customer inquiries. The agent's performance has been inconsistent, with occasional incorrect responses. As the solution architect, you need to ensure reliability and drive continuous improvement. What should you do?
A developer is building an application that uses the Computer Use model to automate a web-based workflow. The application sends an API request with a user instruction, receives a response containing an action, executes the action on the computer, and then sends a screenshot back to the model. What is the primary purpose of sending the screenshot in each iteration?
A developer needs to quickly identify the main points in a collection of unstructured text documents. Which Azure AI capability should they use?
A developer is migrating a repository from the Chat Completions API to the Responses API and encounters an authentication error when calling the Azure OpenAI service. Where should they seek help for this issue?
Exam insights and study advice
These topic areas reflect the day-to-day work of building AI applications and agents on Azure. Being able to plan resources, implement generative and agentic patterns, and handle vision, text, and extraction tasks can support roles in cloud AI development, solution architecture, and applied machine learning. Practicing these skills helps you connect Azure services to real project needs.
What this exam covers
Use the published domain weights to plan your study. Practice results do not predict your certification exam score.
01Implement generative AI and agentic solutions
Topics
- Build generative applications by using Foundry
- Build agents by using Foundry
- Optimize and operationalize generative AI systems
Learning objectives
- Build generative applications by using Foundry
- Build agents by using Foundry
- Optimize and operationalize generative AI systems
02Plan and manage an Azure AI solution
Topics
- Choose the appropriate Foundry services for generative AI and agents
- Set up AI solutions in Foundry
- Manage, monitor, and secure AI systems
- Implement responsible AI across generative AI and agentic systems
Learning objectives
- Choose the appropriate Foundry services for generative AI and agents
- Set up AI solutions in Foundry
- Manage, monitor, and secure AI systems
- Implement responsible AI across generative AI and agentic systems
03Implement computer vision solutions
Topics
- Design and implement image- and video-generation solutions
- Design and implement multimodal understanding workflows
- Implement responsible AI for multimodal content
Learning objectives
- Design and implement image- and video-generation solutions
- Design and implement multimodal understanding workflows
- Implement responsible AI for multimodal content
04Implement information extraction solutions
Topics
- Build retrieval and grounding pipelines
- Extract content from documents
Learning objectives
- Build retrieval and grounding pipelines
- Extract content from documents
05Implement text analysis solutions
Topics
- Apply language model text analysis
- Implement speech solutions
Learning objectives
- Apply language model text analysis
- Implement speech solutions