AI/ML

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.