Chapter 01: Introduction to Artificial Intelligence

• AI vs ML vs Deep Learning vs Generative AI; everyday AI use cases; predictive vs generative
  models.
• Compare outputs from a generative AI model using different business questions.
• Learner questions, quick recap and knowledge check.

Chapter 02: Responsible AI & Model Fundamentals

• Fairness, reliability, safety, privacy, inclusion, transparency, accountability; tokens, parameters,
  context windows.
• Test ambiguous prompts, identify biased or unreliable responses, improve prompts.
• Learner questions, quick recap and knowledge check.

Chapter 3: AI Workloads & Capabilities

• NLP, sentiment analysis, entity extraction, key phrases, summarization; speech, vision,
  multimodal AI.
• Analyze sample customer feedback and extract sentiment, entities and summaries.
• Learner questions, quick recap and knowledge check.

Chapter 4: Introduction to Microsoft Foundry

• Foundry portal, projects, model catalog, deployments, endpoints, APIs, authentication.
• Explore a Foundry project, compare available models, and test a model in the playground.
• Learner questions, quick recap and knowledge check.

Module learning outcome: Understand AI concepts, responsible AI principles and the basic Microsoft
Foundry environment.

Chapter 05: Generative AI & Foundation Models

• LLMs, foundation models, inference, temperature, tokens and context windows.
• Compare model responses by changing prompts and supported parameters.
• Learner questions, quick recap and knowledge check.

Chapter 06: Prompt Engineering Techniques

• System and user prompts, role instructions, few-shot prompting, grounding, structured output.
• Create prompts for FAQ responses, customer support and JSON generation.
• Learner questions, quick recap and knowledge check.

Chapter 07: Build a Generative AI App with Python

• Foundry SDK, endpoints, credentials, API requests and responses.
• Create a basic Python application that submits a prompt and displays an AI response.
• Learner questions, quick recap and knowledge check.

THEORY + PRACTICAL
Chapter 08: Evaluate & Improve AI Responses

• Response relevance, accuracy, hallucinations, safety, prompt evaluation.
• Test a set of prompts and revise instructions to improve answer quality.
• Learner questions, quick recap and knowledge check.

Module learning outcome: Build a simple Python-powered generative AI application and improve its
responses through prompting and evaluation.

Ch 09: AI-901 Sample Questions – Part 1

• Review AI concepts, Responsible AI, model capabilities and foundational terminology.
• Solve and discuss 8–10 scenario-based practice questions.
• Learner questions, quick recap and knowledge check.

Ch 10: AI-901 Sample Questions – Part 2

• Review Foundry concepts, model deployment, prompting and application development.
• Solve 8–10 implementation-oriented practice questions and review answers.
• Learner questions, quick recap and knowledge check.

Ch 11: Build an AI Agent using Microsoft Foundry

• Agent architecture, instructions, tools, workflows, use cases and limitations.
• Create and test a single AI agent for a student course enquiry scenario.
• Learner questions, quick recap and knowledge check.

Ch 12: Text Analytics & Natural Language AI

• Sentiment analysis, named entities, key phrases, text classification and summarization.
• Build a lightweight Python text-analysis flow using an available model or service.
• Learner questions, quick recap and knowledge check.

Module learning outcome:
Strengthen exam readiness and create a basic AI agent and language-analysis workflow.

Chapter 13: Speech AI Solutions

• Speech-to-text, text-to-speech, speech recognition, synthesis and multimodal speech.
• Convert a short audio recording to text and generate synthesized speech.
• Learner questions, quick recap and knowledge check.

Chapter 14: Computer Vision & Multimodal AI

• Image interpretation, image analysis, vision-language models and limitations.
• Upload a sample image and extract descriptive information using a vision-capable model.
• Learner questions, quick recap and knowledge check.

Chapter 15: AI Image Generation
THEORY + PRACTICAL

• Text-to-image generation, prompt specificity, model capabilities, accuracy and responsible use.
• Generate images from descriptive prompts and compare results.
• Learner questions, quick recap and knowledge check.

Chapter 16: Content Understanding & Document AI

• Document extraction, structured information, forms, images, audio and video.
• Extract fields such as invoice number, date, vendor and amount into JSON using an available
  extraction tool.
• Learner questions, quick recap and knowledge check.

Module learning outcome:
Demonstrate how AI processes text, speech, images and business documents.

Chapter 17: Python Integration with Foundry

• SDK and API integration, authentication, error handling, model calls.
• Build a Python script to receive user input and return a model-generated response.
• Learner questions, quick recap and knowledge check.

Chapter 18: Real-Time Project: AI Student Assistant

• Solution architecture, agent instructions, FAQ workflow, structured responses.
• Configure a simple AI assistant to answer questions about courses, schedules and training modes.
• Learner questions, quick recap and knowledge check.

Chapter 19: Responsible Deployment & Solution Review

• Security, privacy, model selection, evaluation, operational cost considerations.
• Review the student assistant, test incorrect inputs, improve safety and response quality.
• Learner questions, quick recap and knowledge check.

Chapter 20: AI-901 Final Assessment & Certification Preparation

• Exam skills review, common mistakes, scenario-based reasoning and revision strategy.
• Complete a short mock assessment and review incorrect answers interactively.
• Learner questions, quick recap and knowledge check.
• Integrate core skills into a small working solution and prepare for the AI-901 certification exam.

•  End-to-End Real-Time Projects & Solutions
• 100% Hands-on Practical Sessions
• Interview Preparation
• Assignments & Practice Labs
• 100% Practical • No Unnecessary Theory
• Learn Like You’re Working in a Real Company