101 Labs - Microsoft Azure AI Fundamentals
The Microsoft Certified: Azure AI Fundamentals certification is an entry-level certification exam aimed at beginners who want to learn about Artificial Intelligence. It’s designed to validate your foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts, and Microsoft Azure services implement them on their infrastructure.
It’s ideal for individuals with both technical and non-technical backgrounds who want to understand the fundamentals of AI and Azure’s AI capabilities.
Updated for the latest exam syllabus:
- AI-900
Azure AI Fundamentals Certification Training
Key aspects of the Azure AI certification:
Target Audience: This certification is ideal for beginners who want to gain a solid foundation in AI principles and concepts, including students looking to enhance their AI skills or anyone interested in creating simple AI projects.
Topics Covered: The certification covers essential Azure AI topics, including:
- Describe Artificial Intelligence Workloads and Considerations (15-20%)
- Describe Fundamental Principles of Machine Learning on Azure (35-40%)
Explore ML types: regression, classification, and clustering. - 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 (15-20%)
Lab Training
Using a free trial of Azure, you will complete follow-along labs with your trainer and cover many exam syllabus topics, including:
- Identify common AI workloads (e.g., anomaly detection, chatbots, image recognition).
- Understand AI concepts like machine learning, computer vision, and natural language processing (NLP).
- Learn Microsoft’s responsible AI principles (fairness, transparency, inclusion, etc.).
- Understand Azure Machine Learning tools, model training, and deployment basics.
- Learn about Azure AI Vision services for image classification, object detection, and optical character recognition (OCR).
- Cover Azure tools like Azure AI Language for text analysis, sentiment analysis, and translation.
- Explore QnA Maker and Azure Bot Service for conversational AI.
- Understand generative AI concepts and Azure tools like Azure OpenAI for text, image, or content generation.
- Address responsible use, including identifying and mitigating risks.
Exam Details:
- Exam Code: AI-900
- Format: 35-45 questions within 60 minutes
- Prerequisites: None, anyone can take the exam
- Passing score: 700/1000
- Validity Period: Lifetime
- Languages: English, Japanese
- Cost: $99
This course is 100% hands-on training. Please refer to a study guide or theory course if you need to learn Azure AI principles beforehand.
Taking This Course:
AI knowledge is essential in today’s IT markets, and even foundational knowledge will take you a long way. Passing this exam demonstrates:
- An appreciation of core artificial intelligence concepts, including machine learning, computer vision, natural language processing, and generative AI.
- Azure AI Proficiency: Familiarity with Microsoft Azure AI services, such as Azure AI Vision, Azure AI Language, and Azure OpenAI, and their practical applications.
- Machine Learning Basics: Grasp of fundamental machine learning principles, including regression, classification, and clustering, and how to apply them using Azure tools.
- Workload Recognition: Ability to identify common AI workloads, like anomaly detection, chatbots, image recognition, and text analysis, and match them to Azure solutions.
- Responsible AI Awareness: Knowledge of Microsoft’s responsible AI principles, such as fairness, transparency, inclusion, and mitigating risks in AI deployment.
- Career Readiness: Entry-level competence in AI and Azure, signaling readiness for roles involving AI solutions or further study for advanced certifications.
- Practical Insight: Understanding of how to leverage Azure tools for tasks like object detection, sentiment analysis, text generation, and conversational AI.
Typical Job Roles
This is an associate-level certification, but also a recognised credential for AI roles. Consider:
- AI Product Support Associate: Provides technical support for AI-driven products, leveraging knowledge of Azure AI Vision, Language, or generative AI tools.
- Business Intelligence Analyst (Junior): Uses Azure AI tools to process and interpret data, aiding decision-making with insights from sentiment analysis or anomaly detection.
- Technical Sales Representative (AI Focus): Sells Azure AI solutions by demonstrating their value, requiring an understanding of AI workloads and responsible AI principles.
- Machine Learning Operations Assistant: Supports the deployment, monitoring, and maintenance of basic machine learning models using Azure Machine Learning.
- Chatbot Developer (Entry-Level): Builds and tests simple conversational AI solutions using Azure Bot Service and QnA Maker for customer service or internal use.
- IT Support Specialist (AI Integration): Assists in integrating Azure AI services into existing IT systems, ensuring functionality for tasks like OCR or text analysis.
- AI Research Assistant: Supports researchers by preparing data, running basic Azure AI experiments, or documenting outcomes of machine learning or generative AI projects.
These roles often serve as starting points, and with experience or further certifications (e.g., AI-102 or DP-100), you can progress to advanced positions like AI Engineer, Data Scientist, or Machine Learning Engineer. Job titles and responsibilities vary by organization, industry, and region, so check job postings for specific requirements.
| Module 1 | AI Foundations |
|---|---|
| Unit 1 | Create a Microsoft Azure Account |
| Unit 2 | Background for AI Overview |
| Unit 3 | Setting Up a Language Service |
| Unit 4 | Background for Computer Vision |
| Unit 5 | Background for Generative AI |
| Unit 6 | Background for NLP |
| Unit 7 | Create AI Service |
| Unit 8 | Create Computer Vision Service |
| Unit 9 | Computer Vision Service Overview and Vision Studio |
| Unit 10 | Install Python |
| Unit 11 | Install Pycharm |
| Module 2 | Computer Vision |
| Unit 1 | Image Captioning(vision studio) |
| Unit 2 | OCR(vision studio) |
| Unit 3 | Facial Recognition(vision studio) |
| Unit 4 | Test CV service via API(Pt 1) |
| Unit 5 | Test CV service via API(Pt 2) |
| Unit 6 | Test Computer Vision Resources(PT 1) |
| Unit 7 | Test Computer Vision Resources(PT 2) |
| Unit 8 | Classify Documents via API(PT 1) |
| Unit 9 | Classify Documents via API(PT 2) |
| Unit 10 | Classify Documents via API(PT 3) |
| Module 3 | Generative AI |
| Unit 1 | Create OpenAI Service |
| Unit 2 | Explore OpenAI Service(AI Foundry) |
| Unit 3 | Use GPT-4 in AI Foundry |
| Unit 4 | DALLE in Azure AI Foundry |
| Unit 5 | Sora in Azure AI Foundry |
| Module 4 | AI Overview |
| Unit 1 | Create Machine Learning Workspace |
| Unit 2 | Explore Machine Learning Workspace |
| Unit 3 | Use Mistral in Azure ML Studio |
| Unit 4 | Clone Sample Notebooks |
| Unit 5 | Create Notebook Compute |
| Unit 6 | Explore Data Notebook(Pt 1) |
| Unit 7 | Explore Data Notebook(Pt 2) |
| Unit 8 | Explore Data Notebook(Pt 3) |
| Unit 9 | Pipeline Notebook(Pt 1) |
| Unit 10 | Pipeline Notebook(Pt 2) |
| Unit 11 | Pipeline Notebook(Pt 3) |
| Unit 12 | Pipeline Notebook(Pt 4) |
| Unit 13 | Pipeline Notebook(Pt 5) |
| Unit 14 | Custom Notebook(Pt 1) |
| Unit 15 | Custom Notebook(Pt 2) |
| Unit 16 | Custom Notebook(Pt 3) |
| Module 5 | Generative AI |
| Unit 1 | Reinforcement Learning Notebook(Pt 1) |
| Unit 2 | Reinforcement Learning Notebook(Pt 2) |
| Unit 3 | Reinforcement Learning Notebook(Pt 3) |
| Unit 4 | Reinforcement Learning Notebook(Pt 4) |
| Unit 5 | Reinforcement Learning Notebook(Pt 5) |
| Unit 6 | Automated Machine Learning in ML Studio |
| Module 6 | NLP |
| Unit 1 | Create Document Intelligence Service |
| Unit 2 | Document Intelligence Service Demos(Pt 1) |
| Unit 3 | Document Intelligence Service Demos(Pt 2) |
| Unit 4 | Document Intelligence Service Demos(Pt 3) |
| Unit 5 | Document Intelligence Service Demos(Pt 4) |
| Module 7 | AI Overview |
| Unit 1 | Create Search Service |
| Unit 2 | Import Data Into Search Service |
| Unit 3 | Use Data in Search Service |
| Module 8 | NLP |
| Unit 1 | Create Translation Service |
| Unit 2 | Translator Service Demos(Pt 1) |
| Unit 3 | Translator Service Demos(Pt 2) |
| Unit 4 | Translator Service Demos(Pt 3) |
| Unit 5 | Translator Service Document Demos(Pt 1) |
| Unit 6 | Translator Service Document Demos(Pt 2) |
| Unit 7 | Create Speech Service |
| Unit 8 | Speech Studio Demos(Pt 1) |
| Unit 9 | Speech Studio Demos(Pt 2) |
| Unit 10 | Speech Service Demos(Pt 1) |
| Unit 11 | Speech Service Demos(Pt 2) |
| Unit 12 | Speech Service Demos(Pt 3) |
| Module 9 | Computer Vision |
| Unit 1 | Create Face Service |
| Unit 2 | Face Service Demos(Pt 1) |
| Unit 3 | Face Service Demos(Pt 2) |
| Unit 4 | Create AI Video Indexer Service |
| Unit 5 | Using AI Indexer Portal(Pt 1) |
| Unit 6 | Using AI Indexer Portal(Pt 2) |
| Unit 7 | Using AI Indexer Portal(Pt 3) |
| Unit 8 | Create Custom Vision Service |
| Unit 9 | Using Custom Vision Portal(Pt 1) |
| Unit 10 | Using Custom Vision Portal(Pt 2) |
| Unit 11 | Using Custom Vision Portal(Pt 3) |
| Unit 12 | Using Custom Vision Service Via IDE(Pt 1) |
| Unit 13 | Using Custom Vision Service Via IDE(Pt 2) |
| Unit 14 | Using Custom Vision Service Via IDE(Pt 3) |
| Unit 15 | Using Custom Vision Service Via IDE(Pt 4) |
| Unit 16 | Create AI foundry Service |
| Unit 17 | Explore AI Foundry and Create Hub |
| Module 10 | Generative AI |
| Unit 1 | Content Understanding in Azure Foundry(Pt 1) |
| Unit 2 | Content Understanding in Azure Foundry(Pt 2) |
| Unit 3 | Content Understanding in Azure Foundry(Pt 3) |
| Unit 4 | Speech Playground in AI Foundry(Pt 1) |
| Unit 5 | Speech Playground in AI Foundry(Pt 2) |
| Unit 6 | Speech Playground in AI Foundry(Pt 3) |
| Unit 7 | Fine-Tuning in AI Foundry(Pt 1) |
| Unit 8 | Fine-Tuning in AI Foundry(Pt 2) |
| Unit 9 | Create an Agent in AI Foundry |
| Unit 10 | Using AI Foundry Agents(Pt 1) |
| Unit 11 | Using AI Foundry Agents(Pt 2) |
| Unit 12 | Using AI Foundry Agents(Pt 3) |
| Unit 13 | Using AI Foundry Agents(Pt 4) |
| Unit 14 | Prompt Flow in AI Foundry(Pt 1) |
| Unit 15 | Prompt Flow in AI Foundry(Pt 2) |
| Unit 16 | Evaluating a Model in AI Foundry |
| Module 11 | Exams |
| Unit 1 | Exam 1 |
| Unit 2 | Exam 2 |
| Unit 3 | Exam 3 |
| Unit 4 | Exam 4 |
