AI-103 Developing AI Apps and Agents on Azure Exam Topics and Questions

These Microsoft Developing AI Apps and Agents on Azure (AI-103) exam topics are organized according to official exam domains to help candidates quickly verify coverage and focus on assessment rather than theory. Each domain is paired with topic-wise AI-103 sample questions that reflect how objectives are tested in the actual exam. This structure enables efficient review, targeted self-assessment, and rapid identification of weak areas when preparing for the Microsoft Developing AI Apps and Agents on Azure certification exam.

Let's Practice Free Microsoft AI-103 Questions Aligned with Official Exam Topics

📄 Exam Contains: 5 Topics

Topic Content

Choose the appropriate Foundry services for generative AI and agents by selecting suitable models for each task, including large language models, small language models, multimodal models, and Foundry Tools. Identify the right Foundry services for generative tasks, grounding, vector search, agent workflows, and multimodal processing while determining appropriate methods for retrieval and indexing. Integrate memory, tool, and knowledge services that support agent solutions effectively. Set up AI solutions in Foundry by designing Azure infrastructure for AI applications and agent-based solutions,... See More

Sample Questions for Topic 1 : Plan and manage an Azure AI solution

Q1 You are setting up security for an AI solution in Azure Foundry and need to implement keyless authentication without exposing credentials. Which security measure should you configure?

Role-based access control (RBAC) only Managed identity with keyless credentials Private networking only Safety filters and content moderation

Topic Content

Build generative applications by using Foundry to deploy and consume various LLM types including large language models, small models, code models, and multimodal models. Implement retrieval-augmented generation RAG capabilities within applications and design sophisticated workflows that incorporate tool-augmented flows and multistep reasoning pipelines. Evaluate the performance of models and applications by detecting fabrications, assessing relevance, ensuring quality standards, and maintaining safety protocols. Integrate generative workflows seamlessly into applications using Foundry SDKs and connectors while configuring applications to establish proper connections... Build generative applications by using Foundry to deploy and consume various LLM types including large language models, small models, code models, and multimodal models. Implement retrieval-augmented generation RAG capabilities within applications and design sophisticated workflows that incorporate tool-augmented flows and multistep reasoning pipelines. Evaluate the performance of models and applications by detecting fabrications, assessing relevance, ensuring quality standards, and maintaining safety protocols. Integrate generative workflows seamlessly into applications using Foundry SDKs and connectors while configuring applications to establish proper connections with Foundry projects. Build intelligent agents using Foundry by defining clear agent roles, goals, conversation-tracking methodologies, and tool schemas that enable effective operation. Create agents that combine retrieval mechanisms, function-calling capabilities, and conversation memory to deliver contextual responses. Integrate diverse agent tools such as APIs, knowledge stores, search functionality, content understanding systems, and custom functions to expand agent capabilities. Implement orchestrated multi-agent solutions and develop autonomous or semiautonomous workflows with appropriate safeguards and approval flow controls. Monitor deployed agents through integrated observability, evaluate agent behavior patterns, and conduct thorough error analysis to ensure reliability and performance. Optimize and operationalize generative AI systems by tuning generation behavior through prompt engineering and adjusting model parameters to achieve desired outputs. Implement advanced techniques including model reflection, chain-of-thought evaluations, and self-critique loops to enhance reasoning quality. Establish comprehensive observability by implementing tracing mechanisms, token analytics, safety signals, and latency breakdowns to monitor system performance. Orchestrate multiple models, flows, or hybrid combinations of LLM and rules engines to create robust and flexible AI solutions that meet diverse business requirements. See More

Topic Content

Build retrieval and grounding pipelines to implement information extraction solutions by ingesting and indexing diverse content types including documents, images, audio, and video. Configure multiple search capabilities such as semantic search, hybrid search, and vector search to enable effective grounding of your data. Implement content enrichment using both custom and built-in skills designed for processing text, images, and layout information. Establish RAG ingestion flows that incorporate document processing and optical character recognition OCR technology to extract and prepare data for... See More

Topic Content

Implement Computer Vision Solutions Design and implement image and video generation solutions by creating systems that generate images from text prompts and reference media, as well as developing video generation capabilities from textual descriptions and source materials. Configure advanced image-editing workflows including inpainting techniques, mask-based modifications, and prompt-driven adjustments to refine generated content. Implement comprehensive video editing workflows to modify and enhance generated video outputs while selecting and applying appropriate generation and editing controls available through the platform. Additionally, design and... See More

Topic Content

Apply language model text analysis to implement solutions that extract entities, topics, summaries, and structured JSON outputs using generative prompting and Foundry Tools. Configure detection of sentiment, tone, safety issues, and sensitive content within text data. Build solutions that translate text using Azure Translator in Foundry Tools or LLM-powered translation flows to support multiple languages. Customize language model outputs for domain-specific tasks such as compliance summarization and domain extraction. Implement speech solutions by creating workflows that convert speech to text... See More

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AI-103 Developing AI Apps and Agents on Azure Exam Topics and Questions

Let's Practice Free Microsoft AI-103 Questions Aligned with Official Exam Topics

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