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Microsoft Certified: Azure AI Apps and Agents Developer Associate (AI-103) Syllabus

Plan and manage an Azure AI solution

Selecting Foundry services and models, setting up deployments, and securing, monitoring and governing AI systems responsibly.

28%
Weight
17
Questions
28
Marks

Choose Foundry Services and Models

  • Selecting LLMs, small language models and multimodal models
  • Foundry services for grounding, vector search and agent workflows
  • Retrieval and indexing approach selection
  • Memory, tool and knowledge integration for agents

Set Up AI Solutions in Foundry

  • Azure infrastructure design for AI apps and agents
  • Deployment options for models and agents
  • Configuring model and agent deployments
  • CI/CD integration for Foundry projects

Manage, Monitor and Secure AI Systems

  • Quotas, rate limits, scaling and cost control
  • Monitoring drift, safety events and grounding quality
  • Search index health and relevance monitoring
  • Managed identity, keyless credentials and private networking
  • Role-based access policies

Responsible AI for Generative and Agentic Systems

  • Content safety filters and guardrails
  • Evaluators and safety evaluations
  • Trace logging, provenance metadata and approval workflows
  • Agent oversight modes and tool-access controls

Implement generative AI and agentic solutions

Building, evaluating and operationalizing generative AI applications and single- or multi-agent solutions with Foundry.

33%
Weight
20
Questions
33
Marks

Build Generative Applications with Foundry

  • Deploying and consuming LLMs, code and multimodal models
  • Retrieval-augmented generation (RAG) in applications
  • Tool-augmented and multistep reasoning workflows
  • Evaluating apps for fabrications, relevance, quality and safety
  • Connecting apps to a Foundry project with Foundry SDKs

Build Agents with Foundry

  • Agent roles, goals, conversation tracking and tool schemas
  • Retrieval, function calling and conversation memory
  • Integrating APIs, search and custom functions as agent tools
  • Orchestrated multi-agent solutions
  • Safeguards and approval flows for autonomous workflows
  • Agent monitoring, evaluation and error analysis

Optimize and Operationalize Generative AI

  • Prompt engineering and model parameter tuning
  • Reflection, chain-of-thought evaluation and self-critique loops
  • Tracing, token analytics and latency breakdowns
  • Orchestrating multiple models and hybrid rules engines

Implement computer vision solutions

Generating and editing images and video and building multimodal visual understanding with responsible AI controls.

13%
Weight
8
Questions
13
Marks

Image and Video Generation

  • Text-to-image generation with reference media
  • Text-to-video generation
  • Inpainting and mask-based image edits
  • Editing generated videos
  • Generation and editing controls

Multimodal Understanding Workflows

  • Image captioning and visual question answering
  • Alt-text and accessible image descriptions
  • Azure Content Understanding in Foundry Tools for visual extraction
  • Video analysis and object or region detection
  • Single-task and pro-mode Content Understanding pipelines

Responsible AI for Multimodal Content

  • Classifying unsafe visual content
  • Indirect prompt injection through embedded image text
  • Watermarks and brand or symbol policy rules
  • Flagging potentially inappropriate content

Implement text analysis solutions

Applying language models and Foundry Tools to text analysis, translation and speech workloads.

13%
Weight
8
Questions
13
Marks

Language Model Text Analysis

  • Entity, topic and summary extraction to structured JSON
  • Sentiment, tone and sensitive content detection
  • Azure Translator in Foundry Tools and LLM translation
  • Customizing outputs for domain tasks such as compliance summaries

Speech Solutions

  • Speech to text and text to speech for agents
  • Speech as an agent modality and custom speech models
  • Multimodal reasoning over audio input
  • Speech translation with language models and Foundry Tools

Implement information extraction solutions

Building retrieval and grounding pipelines and extracting structured content from documents with Content Understanding.

13%
Weight
7
Questions
13
Marks

Retrieval and Grounding Pipelines

  • Ingesting and indexing documents, images, audio and video
  • Semantic, hybrid and vector search for grounding
  • Built-in and custom enrichment skills
  • RAG ingestion with OCR
  • Connecting retrieval to agent tools

Document Content Extraction

  • OCR, layout analysis and field extraction pipelines
  • Grounded representations for agents and RAG
  • Content Understanding analyzers for structured or markdown output
  • Preparing extracted content for downstream reasoning

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Learning Progress

Plan and manage an Azure AI solution 85%
Implement generative AI and agentic solutions 92%

Practice Test Scores

95%
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