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

Planning and Managing Azure AI Solutions

Covers selecting the right Foundry models and services, deployment options, quotas/scaling, cost control, authentication, and monitoring.

28%
Weight
15
Questions
280
Marks

Foundry Service and Model Selection

  • Choosing the right model class: LLM, small language model, multimodal, Foundry Tools
  • Selecting services for grounding, vector search, agent workflows, and multimodal processing
  • Retrieval and indexing method selection
  • Memory, tool, and knowledge integration services for agents

Deployment, Cost, and Security

  • Deployment options, quotas, and scaling in Azure AI Foundry
  • Cost control strategies for model and agent deployments
  • Managed identity, private networking, and keyless credentials
  • Azure Key Vault for managing AI service secrets and keys
  • Monitoring: Azure Monitor, Log Analytics, diagnostics logs

Implementing Generative AI and Agentic Solutions

The largest domain on AI-103 — covers building generative applications and multi-agent solutions using Azure AI Foundry, RAG, tool calling, and responsible-AI production controls.

33%
Weight
18
Questions
330
Marks

Building Generative Applications

  • Deploying and consuming LLMs, small models, code models, and multimodal models via Foundry
  • Retrieval-augmented generation (RAG) pipeline design
  • Tool-augmented flows and multistep reasoning pipelines
  • Prompt engineering: system messages, few-shot, chain-of-thought, temperature/top-p

Multi-Agent Orchestration

  • Multi-agent orchestration and tool calling
  • Conversation memory and approval-gated workflows
  • Function calling: connecting models to external APIs
  • Semantic Kernel and LangChain integration patterns

Responsible AI in Production

  • Content safety filters and prompt shields (jailbreak/injection detection)
  • Evaluators for fabrications, relevance, quality, and safety
  • Trace logging and provenance metadata
  • Evaluating apps and agents through Foundry SDKs and connectors

Implementing Computer Vision Solutions

Covers Azure AI Vision, image analysis, optical character recognition, custom vision, and multimodal image/video understanding.

13%
Weight
7
Questions
130
Marks

Image and Video Analysis

  • Azure AI Vision Image Analysis: dense captioning, object detection, tagging
  • OCR with Azure AI Vision: Read API for printed and handwritten text
  • Face API: face detection, attribute analysis, and verification
  • Custom Vision: training image classifiers and object detectors
  • Azure Video Indexer: transcription, people detection, scene segmentation
  • Multimodal image/video generation and understanding via Foundry models

Implementing Text Analysis Solutions

Covers Azure AI Language pre-built features, conversational language understanding, and speech/translation services.

13%
Weight
7
Questions
130
Marks

Pre-built Language Features

  • Sentiment analysis and opinion mining
  • Key phrase extraction and entity recognition (NER)
  • PII detection and redaction
  • Text summarisation: extractive vs abstractive
  • Conversational Language Understanding (CLU): intents, entities, utterances

Speech and Translation

  • Speech-to-text: real-time vs batch (with SAS URLs)
  • Text-to-speech with neural voices
  • Speaker recognition: identification and verification
  • Azure AI Translator: text, document, and custom translation
  • Azure OpenAI Realtime API for low-latency two-way voice agents vs classic Speech SDK

Implementing Information Extraction Solutions

Covers Azure AI Document Intelligence, Azure AI Search enrichment pipelines, and grounding for RAG/agent scenarios.

13%
Weight
7
Questions
130
Marks

Document Intelligence

  • Pre-built models: invoice, receipt, business card, ID document, tax form
  • Custom model training: labelled data requirements and confidence thresholds
  • Composed models: combining multiple custom models

Azure AI Search and Grounding

  • AI Search end-to-end skillset chain (OCR → language detection → entity recognition → embedding generation)
  • Semantic and hybrid/vector search
  • Custom skills and knowledge store projections
  • Content Understanding and OCR-based RAG flows

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Learning Progress

Planning and Managing Azure AI Solutions 85%
Implementing Generative AI and Agentic Solutions 92%

Practice Test Scores

95%
Latest Score
Above passing threshold

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