Azure

The complete Document Intelligence and information extraction guide for Azure AI and AI-103. Document Intelligence overview and Form Recognizer rename. Layout model for tables and structure. Read model for raw text. Prebuilt models for invoices receipts ID documents W-2 health insurance credit card mortgage and more. Custom template vs neural models. Composed models for multi-type routing. Custom classifiers for document sorting. Azure AI Search indexing pipeline with data sources skillsets and indexers. Built-in skills OCR NER key phrases language detection Text Split and embedding. Custom skills with Azure Functions. Vector search and integrated vectorization. Content Understanding with natural language schemas and multimodal extraction. Complete document processing pipeline with validation and monitoring. Choosing the right service. Eight common mistakes and seven interview Q and As.

The complete text analysis and speech guide for Azure AI and AI-103. Azure AI Language platform overview. Sentiment analysis with opinion mining for aspect-level insights. Named Entity Recognition with entity linking. PII detection and redaction for compliance. Key phrase extraction and language detection. Extractive vs abstractive summarization. Conversational Language Understanding CLU replacing LUIS with intents and entities. Custom text classification and custom NER. Text Analytics for Health. Azure AI Translator with text document and custom modes. Speech-to-text with diarization and custom speech. Text-to-speech with neural voices and SSML. Speech translation. Choosing the right service. Eight common mistakes and seven interview Q and As.

The complete computer vision guide for Azure and AI-103. Image Analysis 4.0 with captions dense captions tags object detection people detection smart crop and background removal. Custom Vision for training your own classifiers and detectors. OCR and Read API for printed and handwritten text extraction. Face API with detection verification identification liveness and responsible use restrictions. Spatial Analysis for people counting zone monitoring and dwell time on edge devices. Content Understanding for multimodal extraction. Choosing the right vision service decision guide. Real-world examples for healthcare retail manufacturing insurance and logistics. Eight common mistakes and seven interview Q and As.

The complete AI Agents guide for Azure and AI-103. What makes an agent different from RAG. The Responses API vs Chat Completions. Azure AI Agent Service managed platform. Built-in tools including file search code interpreter and Bing grounding. Function calling with custom tool schemas and the think-act-observe loop. Code Interpreter for sandboxed Python execution. Agent memory with threads. Five multi-agent orchestration patterns sequential concurrent handoff group chat and Magentic-One. Semantic Kernel and AutoGen framework integration. Agent evaluation dimensions. Building a practical data engineering agent. Eight common mistakes and seven interview Q and As.

The complete RAG pipeline guide for Azure AI and AI-103. RAG architecture in six steps from ingestion to generation. Azure AI Search as the retrieval engine. Creating search indexes with text and vector fields. Indexers and data sources for automated ingestion. Chunking strategies fixed-size sentence semantic and Document Layout. Embedding models text-embedding-3-large and small. Vector search with HNSW and cosine similarity. Semantic search with cross-encoder re-ranking. Hybrid search combining keyword vector and semantic. Integrated vectorization for zero-code ingestion. Skillsets for AI enrichment. On Your Data for quick RAG. Building production RAG pipelines. Evaluating RAG quality. Eight common mistakes and seven interview Q and As.

The complete prompt engineering guide for Azure AI and AI-103. Chat completion anatomy with system user and assistant roles. System messages with role task constraints tone format and safety. Zero-shot prompting for simple tasks. Few-shot prompting with input-output examples. Chain-of-thought for step-by-step reasoning. Temperature and top-p for controlling randomness. Max tokens stop sequences and penalties. JSON mode vs structured outputs with schema enforcement. Grounding with in-prompt RAG and On Your Data. Prompt templates and parameterization. Negative instructions. Prompt ordering and token efficiency. Defending against prompt injection with four defense layers. Testing in Foundry Playground. Eight common mistakes and seven interview Q and As.

The complete security and governance guide for Azure AI solutions and AI-103 Domain 1. Authentication with Entra ID managed identity and API keys. RBAC roles for Foundry. API key rotation. Network security with private endpoints VNet integration and NSGs. Azure AI Content Safety with four harm categories and severity levels. Content filter configuration with annotate and block. Prompt Shields for direct jailbreaks and indirect injection attacks. Groundedness detection for hallucination prevention. Protected material detection for copyright. Custom blocklists. Task adherence for agents. Application Insights tracing and monitoring. Cost management. Microsoft six Responsible AI principles. Eight common mistakes and seven interview Q and As.

The complete Microsoft Foundry platform guide for AI-103. Evolution from Azure AI Studio to Microsoft Foundry. Foundry architecture with Hubs Projects and Foundry Resources. Creating resources and projects. The Model Catalog with 1800 plus models. Deployment types serverless API vs managed compute. Azure OpenAI within Foundry with Standard Provisioned Global and Data Zone. Model selection decision framework. The Foundry Playground for testing. Connections with managed identity. Prompt Flow orchestration. Endpoints and the unified Model Inference API. Quotas TPM limits and cost management. The AI development lifecycle. Eight common mistakes and seven interview Q and As.