AI900 portion

Basics

Skills at a glance

  • Describe Artificial Intelligence workloads and considerations (15–20%)

  • Describe fundamental principles of machine learning on Azure (15–20%)

  • Describe features of computer vision workloads on Azure (15–20%)

  • Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

  • Describe features of generative AI workloads on Azure (20–25%)

Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

  • Identify computer vision workloads

  • Identify natural language processing workloads

  • Identify document processing workloads

  • Identify features of generative AI workloads

Identify guiding principles for responsible AI

  • Describe considerations for fairness in an AI solution

  • Describe considerations for reliability and safety in an AI solution

  • Describe considerations for privacy and security in an AI solution

  • Describe considerations for inclusiveness in an AI solution

  • Describe considerations for transparency in an AI solution

  • Describe considerations for accountability in an AI solution

Describe fundamental principles of machine learning on Azure (15-20%)

Identify common machine learning techniques

  • Identify regression machine learning scenarios

  • Identify classification machine learning scenarios

  • Identify clustering machine learning scenarios

  • Identify features of deep learning techniques

  • Identify features of the Transformer architecture

Describe core machine learning concepts

  • Identify features and labels in a dataset for machine learning

  • Describe how training and validation datasets are used in machine learning

Describe Azure Machine Learning capabilities

  • Describe capabilities of automated machine learning

  • Describe data and compute services for data science and machine learning

  • Describe model management and deployment capabilities in Azure Machine Learning

Describe features of computer vision workloads on Azure (15–20%)

Identify common types of computer vision solution

  • Identify features of image classification solutions

  • Identify features of object detection solutions

  • Identify features of optical character recognition solutions

  • Identify features of facial detection and facial analysis solutions

Identify Azure tools and services for computer vision tasks

  • Describe capabilities of the Azure AI Vision service

  • Describe capabilities of the Azure AI Face detection service

Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

Identify features of common NLP Workload Scenarios

  • Identify features and uses for key phrase extraction

  • Identify features and uses for entity recognition

  • Identify features and uses for sentiment analysis

  • Identify features and uses for language modeling

  • Identify features and uses for speech recognition and synthesis

  • Identify features and uses for translation

Identify Azure tools and services for NLP workloads

  • Describe capabilities of the Azure AI Language service

  • Describe capabilities of the Azure AI Speech service

Describe features of generative AI workloads on Azure (20–25%)

Identify features of generative AI solutions

  • Identify features of generative AI models

  • Identify common scenarios for generative AI

  • Identify responsible AI considerations for generative AI

Identify generative AI services and capabilities in Microsoft Azure

  • Describe features and capabilities of Azure AI Foundry

  • Describe features and capabilities of Azure OpenAI service

  • Describe features and capabilities of Azure AI Foundry model catalog

Study resources

We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.





PART 1: Fundamentals of Artificial Intelligence (AI)

Understand what AI is and its applications

  1. What is AI?

    • Definition of Artificial Intelligence
    • Differences between AI, ML, and DL
    • Common applications of AI in daily life

  2. Types of AI

    • Narrow AI vs General AI
    • Reactive Machines, Limited Memory, Theory of Mind, Self-Aware AI

  3. Key Domains of AI

    • Machine Learning (ML)
    • Natural Language Processing (NLP)
    • Computer Vision
    • Conversational AI

PART 2: Machine Learning (ML) Basics

Understand how machines learn from data

  1. What is Machine Learning?

    • Definition and examples
    • How ML differs from traditional programming

  2. Types of Machine Learning

    • Supervised Learning
    • Unsupervised Learning
    • Reinforcement Learning

  3. Common ML Algorithms (basic understanding)

    • Classification (e.g., Decision Trees, Logistic Regression)
    • Regression
    • Clustering (e.g., K-Means)

  4. ML Model Lifecycle

    • Data collection
    • Data preprocessing
    • Model training
    • Model evaluation
    • Deployment

  5. Evaluation Metrics

    • Accuracy, Precision, Recall, F1 Score
    • Confusion Matrix

PART 3: Computer Vision Basics

Teaching computers to "see" and analyze images

  1. What is Computer Vision?

    • Real-life applications: facial recognition, object detection

  2. Common Computer Vision Tasks

    • Image classification
    • Object detection
    • Semantic segmentation
    • Optical character recognition (OCR)

  3. Azure Services for Computer Vision

    • Azure Computer Vision API
    • Azure Custom Vision

PART 4: Natural Language Processing (NLP) Basics

Teaching machines to understand human language

  1. What is NLP?

    • Everyday uses: chatbots, translators, sentiment analysis

  2. Common NLP Tasks

    • Text classification
    • Named Entity Recognition (NER)
    • Language translation
    • Sentiment analysis

  3. Azure Services for NLP

    • Azure Text Analytics
    • Azure Translator
    • Azure Language Understanding (LUIS)

 PART 5: Conversational AI

Understanding chatbots and voice assistants

  1. What is Conversational AI?

    • Chatbots vs Voice assistants

  2. Azure Services for Conversational AI

    • Azure Bot Services
    • QnA Maker
    • Integrating with Teams, Web apps

PART 6: AI on Azure

Using Microsoft Azure for building and deploying AI solutions

  1. Azure AI Services Overview

    • Cognitive Services
    • Azure Machine Learning
    • Azure AI Studio

  2. Responsible AI Principles

    • Fairness
    • Reliability
    • Privacy and Security
    • Inclusiveness
    • Transparency
    • Accountability

  3. Low-code / No-code AI solutions

    • Using Azure ML Studio (designer)
    • AutoML

PART 7: Responsible AI and Ethics

Understanding the importance of building AI responsibly

  1. Bias in AI
  2. Explainability
  3. Privacy concerns
  4. Sustainability of AI systems

Suggested Learning Order

If you're learning step-by-step:

  1. Start with What is AI and Types of AI
  2. Learn Machine Learning basics
  3. Explore Computer Vision and NLP
  4. Understand Conversational AI
  5. Dive into Azure AI tools
  6. Finish with Responsible AI

After completing all AI-900 topics listed above, you're already well-prepared for the exam. But if you want to go beyond AI-900 (for deeper learning, projects, interviews, or further certifications), here’s what else you can cover, based on your interest and career path:

🔁 1. Deepen Your Machine Learning Knowledge

Go beyond basics and understand the math & techniques:

  • Linear Regression & Logistic Regression (in detail)
  • Decision Trees, Random Forests, SVM
  • Gradient Descent
  • Overfitting & Underfitting
  • Cross-validation
  • Hyperparameter tuning

➡️ Tools: scikit-learn, Jupyter Notebook, pandas, matplotlib
➡️ Optional: Learn Python if you haven’t already

🧠 2. Learn Deep Learning (DL)

This is useful if you're interested in image, video, or audio analysis:

  • Neural Networks Basics
  • Forward & Backpropagation
  • Activation Functions
  • Convolutional Neural Networks (CNNs) – for images
  • Recurrent Neural Networks (RNNs), LSTMs – for sequences/text
  • Transformers – for NLP & Generative AI

➡️ Tools: TensorFlow, Keras, PyTorch

🗣️ 3. Advanced Natural Language Processing

  • Tokenization & Word Embeddings (Word2Vec, GloVe)
  • Text Summarization
  • Named Entity Recognition (NER)
  • Question Answering Systems
  • Large Language Models (LLMs) – GPT, BERT

➡️ Tools: Hugging Face Transformers, spaCy, NLTK

👁️ 4. Advanced Computer Vision

  • Transfer Learning using pre-trained models
  • Object detection (YOLO, SSD)
  • Image Segmentation (U-Net)
  • Face detection & recognition
  • Real-time video analytics

☁️ 5. More Azure AI Services (hands-on)

  • Azure AI Studio for LLMs
  • Azure Form Recognizer
  • Azure Speech Services – speech-to-text, text-to-speech
  • Azure ML Pipelines – for production workflows

🔐 6. Real-World AI Applications & Projects

  • Build end-to-end projects (data prep, model, API, deployment)
  • Example Projects:
    • Spam detection
    • Face mask detector
    • Chatbot for college helpdesk
    • Resume parser using NLP
    • AI-powered virtual assistant

📜 7. Certification & Career Path After AI-900

If you want certifications:

  • DP-100: Designing and Implementing a Data Science Solution on Azure
  • PL-300: Microsoft Power BI Data Analyst (data focus)
  • AI-102: Designing and Implementing an Azure AI Solution
  • Google Cloud ML Engineer or AWS ML Specialty

🛠️ Optional but Valuable Skills

  • Python programming (intermediate)
  • SQL (for data querying)
  • Data visualization using Power BI or Tableau
  • Version control (Git & GitHub)
  • APIs & JSON (for integrating AI models)


🔁 1. Deepen Your Machine Learning Knowledge

Go beyond basics and understand the math & techniques:

  • Linear Regression & Logistic Regression (in detail)
  • Decision Trees, Random Forests, SVM
  • Gradient Descent
  • Overfitting & Underfitting
  • Cross-validation
  • Hyperparameter tuning

➡️ Tools: scikit-learn, Jupyter Notebook, pandas, matplotlib
➡️ Optional: Learn Python if you haven’t already

🧠 2. Learn Deep Learning (DL)

This is useful if you're interested in image, video, or audio analysis:

  • Neural Networks Basics
  • Forward & Backpropagation
  • Activation Functions
  • Convolutional Neural Networks (CNNs) – for images
  • Recurrent Neural Networks (RNNs), LSTMs – for sequences/text
  • Transformers – for NLP & Generative AI

➡️ Tools: TensorFlow, Keras, PyTorch

🗣️ 3. Advanced Natural Language Processing

  • Tokenization & Word Embeddings (Word2Vec, GloVe)
  • Text Summarization
  • Named Entity Recognition (NER)
  • Question Answering Systems
  • Large Language Models (LLMs) – GPT, BERT

➡️ Tools: Hugging Face Transformers, spaCy, NLTK

👁️ 4. Advanced Computer Vision

  • Transfer Learning using pre-trained models
  • Object detection (YOLO, SSD)
  • Image Segmentation (U-Net)
  • Face detection & recognition
  • Real-time video analytics

☁️ 5. More Azure AI Services (hands-on)

  • Azure AI Studio for LLMs
  • Azure Form Recognizer
  • Azure Speech Services – speech-to-text, text-to-speech
  • Azure ML Pipelines – for production workflows

🔐 6. Real-World AI Applications & Projects

  • Build end-to-end projects (data prep, model, API, deployment)
  • Example Projects:
    • Spam detection
    • Face mask detector
    • Chatbot for college helpdesk
    • Resume parser using NLP
    • AI-powered virtual assistant

📜 7. Certification & Career Path After AI-900

If you want certifications:

  • DP-100: Designing and Implementing a Data Science Solution on Azure
  • PL-300: Microsoft Power BI Data Analyst (data focus)
  • AI-102: Designing and Implementing an Azure AI Solution
  • Google Cloud ML Engineer or AWS ML Specialty

🛠️ Optional but Valuable Skills

  • Python programming (intermediate)
  • SQL (for data querying)
  • Data visualization using Power BI or Tableau
  • Version control (Git & GitHub)
  • APIs & JSON (for integrating AI models)