Azure AI Engineer Associate Certification (AI-102) – Full Course to PASS the Exam

Introduction

This tutorial provides a comprehensive guide to preparing for the Azure AI Engineer Associate Certification (AI-102). It covers essential topics, tools, and concepts necessary to design and implement Azure AI solutions. Whether you're aiming to pass the certification exam or enhance your skills in Azure AI services, this guide will walk you through the key areas of focus.

Step 1: Set Up Your Development Environment

To get started, ensure you have a proper development environment set up for Azure AI development.

• Install Required Tools: Make sure you have the latest versions of:

  • Azure CLI
  • Visual Studio Code or your preferred IDE
  • Python (if applicable)

• Create an Azure Account: If you don’t have one, sign up for an Azure account to access the services needed for practice and development.

• Set Up Azure OpenAI Studio:

  • Navigate to Azure OpenAI Studio.
  • Create a new resource for OpenAI services.

Step 2: Understand Key AI Concepts

Familiarize yourself with foundational AI concepts that are crucial for the certification.

• AI vs GenAI: Understand the differences and applications of general AI versus generative AI.
• Foundational Models: Learn about foundational models and their role in AI solutions.
• Large Language Models: Get to grips with what LLMs are and how they function.
• Transformer Architecture: Study the architecture that powers many AI models today.

Step 3: Explore Azure AI Services

Dive into the various Azure services available for AI implementation.

• Azure AI Services Overview:

  • Familiarize yourself with the different Azure AI services, including Cognitive Services.
  • Understand the use of containers and diagnostic logging.

• Setup Azure AI Services API:

  • Go to the Azure portal and create an API for Azure AI Services.
  • Configure necessary permissions and settings.

Step 4: Learn About Prompt Engineering

Prompt engineering is vital for effective interaction with AI models.

• Basic Prompt Engineering:

  • Understand how to formulate prompts to get desired outputs from models.

• Experiment with Completion Models:

  • Adjust parameters like temperature and Top P to see how they influence model responses.

Step 5: Implement Azure AI Search

Azure AI Search allows you to build intelligent search solutions.

• Understand RAG (Retrieval-Augmented Generation):

  • Learn how RAG works and its applications in enhancing search capabilities.

• Explore Semantic Ranking:

  • Get familiar with how Azure AI implements semantic ranking for better search results.

Step 6: Work with Text Analytics and Computer Vision

Gain practical knowledge in processing text and images.

• Text Analytics:

  • Learn about sentiment analysis, entity extraction, and how to identify personally identifiable information (PII).

• Computer Vision:

  • Understand how to use Azure's Computer Vision capabilities for image analysis and optical character recognition (OCR).

Step 7: Explore Document Intelligence

Document processing is a significant part of many AI applications.

• Azure AI Document Intelligence:

  • Familiarize yourself with tools for document understanding and form recognition.

• Custom Vision Projects:

  • Learn about creating custom vision projects, including classification and object detection.

Step 8: Utilize Speech and Language Services

Explore Azure's capabilities for speech recognition and natural language understanding.

• Speech API:

  • Understand how to implement speech recognition in applications.

• Language Understanding (LUIS):

  • Learn about Language Understanding and its integration with QnA Maker for creating intelligent conversational agents.

Conclusion

By following this tutorial, you have a structured approach to prepare for the Azure AI Engineer Associate Certification. Focus on setting up your environment, understanding key AI concepts, exploring Azure services, and working with different AI applications. For further learning, consider practicing with real-world projects and engaging with Azure's documentation and community resources.