Generative AI Engineering Training | Expert-Led Online Course
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What Is Generative AI for Azure Data Engineers?
Quick answer   It is applying data engineering skills to build reliable, secure generative AI applications on Azure.
T H E  D E F I N I T I O N W H A T  T H I S  G U I D E  C O V E R S
Generative AI for Azure data engineers is the practice of This guide moves from fundamentals to advanced 
applying data engineering skills, such as pipelines, applications: core GenAI concepts, key Azure AI services, 
lakehouses, governance and quality, to build reliable AI how GenAI fits into data engineering, embeddings and 
applications on Azure using services like Microsoft vector search, RAG, AI agents, real-world use cases, and 
Foundry, Azure AI Search and Microsoft Fabric. Instead of security and governance, with a practical path for building 
training models from scratch, you prepare trusted data, skills in 2026.
connect it to foundation models and operate the solution 
securely at scale.
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What Are the Fundamentals of Generative AI?
Quick answer   Generative AI uses language models that predict text from context; tokens, prompts, embeddings and limits are the foundation.
1 Large language models 2 Tokens and context 3 Prompts
Models trained on vast text that generate, Models read and write tokens within a limited Clear instructions, examples and constraints 
summarize and transform language. context window that drives cost. shape the quality of outputs.
4 Embeddings 5 Hallucinations 6 RAG vs fine-tuning
Vectors that capture meaning so similar content Models can state wrong facts confidently, so RAG adds fresh data at answer time; fine-tuning 
can be found by search. grounding and checks matter. changes model behavior.
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Which Azure AI Services Should You Know?
Quick answer   Azure offers managed services for models, search, document processing, safety and agents, centered on Microsoft Foundry.
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How Does GenAI Fit into Data Engineering?
Quick answer   GenAI helps data engineers handle unstructured data, speed up pipeline work and make data easier to find and use.
1 Unstructured data 2 Metadata and catalogs 3 Data quality
Turn documents, emails and call transcripts into Generate descriptions, tags and classifications to Flag anomalies, inconsistent values and missing 
structured, queryable data. improve discovery. context for review.
4 Natural language to SQL 5 Code assistance 6 Summarization
Let analysts ask in plain language, with checks Use Copilot tools in Fabric and developer tools Condense long reports, logs and incident notes 
on generated queries. to draft and explain pipeline code. into usable summaries.
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How Do Embeddings and Vector Search Work?
Quick answer   Embeddings turn content into vectors so systems can retrieve by meaning, usually combined with keyword search.
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How Do You Build a RAG App on Azure?
Quick answer   RAG ingests and indexes your data, retrieves relevant passages, and asks a model to answer only from that context.
1 Ingest 2 Index 3 Retrieve
Use Fabric, Data Factory or Databricks pipelines Chunk, embed and load content into a search Query with the user’s question plus filters and 
to collect and clean sources. index with metadata. permissions.
4 Generate 5 Cite 6 Evaluate
Ask a Foundry-deployed model to answer from Return source links and say when the answer is Test groundedness and relevance, then tune 
supplied context only. not in the data. chunks, ranking and prompts.
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How Do AI Agents and Data Agents Work?
Quick answer   Agents add tools and multi-step reasoning; data agents let people query governed data in natural language.
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What Are Real-World Azure Applications?
Quick answer   Real-world Azure GenAI apps focus on knowledge access, document processing and faster analytics.
1 Knowledge assistant 2 Document processing 3 Natural language BI
Answer employee questions from policies, Extract invoice, contract and form data into Ask questions about governed datasets and get 
manuals and wikis with citations. tables automatically. queries or charts.
4 Support copilot 5 Data quality monitor 6 Migration helper
Suggest replies and summarize cases using Explain failed checks and recommend fixes to Explain and convert legacy SQL or ETL code, with 
customer history. data teams. human review.
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How Do You Secure, Govern and Scale GenAI?
Quick answer   Secure GenAI with identity, private networking and governance, and manage cost and quality with evaluation and monitoring.
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Conclusion: What Should You Do Next?
Quick answer   Start with fundamentals and trusted data, build a small RAG app, then add agents, evaluation and governance.
K E Y  TA K E A W AY Y O U R  N E X T  S T E P S
Generative AI is a natural next step for Azure data A practical roadmap: pick one internal knowledge source, 
engineers because AI quality depends on data quality. build a RAG prototype with Azure AI Search and Microsoft 
Start with the fundamentals, learn the key Azure AI Foundry, add access control and evaluation, then explore 
services, build a small RAG application on trusted data, agents and Fabric data agents. Check Microsoft Learn for 
and add agents, evaluation and governance as your current service names and features, since Azure AI evolves 
applications mature. quickly.
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