AI 102 – Microsoft Azure AI Engineer Associate Master Guide

This article includes all the necessary information you need to know before taking the AI 102 – Microsoft Azure AI Engineer Associate Certification exam.
The AI 102 certification exam evaluates the candidate‘s knowledge in Designing and Implementing Microsoft Azure AI Solutions. Preparing for Microsoft AI-102 Certification helps you to expand your real-world knowledge of diverse machine learning (ML) and artificial intelligence (AI) workloads, and how that can be implemented with Azure AI.

What are Azure AI Engineer’s roles and responsibilities?

• As a Microsoft Azure AI engineer, You need to use the Microsoft Bot Framework, Azure Cognitive Search and Azure Cognitive Services, you develop, manage, and deploy AI solutions.
• You are involved in all stages from requirements definition and design down to an AI solution’s development, maintenance, deployment, monitoring, and performance optimization.
• You collaborate with solution architects, using your skills to bring their vision to life.
• You work with IoT experts, data scientists, data engineers, and AI developers on end-to-end AI projects to design full-fledged AI solutions.
• You use REST-based APIs and SDKs to develop your natural language processing (NLP), computer vision, knowledge mining, and conversational AI solutions on Azure.

Why is learning AI important?

According to a recent survey, the global Artificial Intelligence (AI) market is predicted to cross $1812 billion by 2030, indicating that the world is preparing for an AI-driven future. The future demand for Artificial Intelligence (AI) is expected to grow exponentially, impacting the adoption of AI in various industries.

There is now a huge demand from organizations that require AI engineers to design, develop, integrate, and deploy AI solutions across various technology platforms. Hence, relevant IT certifications and knowledge in the area of AI must be possessed by professionals so that you can stand out from the crowd.
The Microsoft Certified – Azure AI Engineer Associate course is ideal for experienced programmers who wish to develop AI-infused applications leveraging Azure services. Learners should be familiar with Python or C#, REST API concepts, JSON,  and Azure services solutions.

Key Advantages of AI 102 Certification

•  You will learn how to plan, build, and manage knowledge mining, conversational AI, computer vision, and natural language processing solutions on Azure by obtaining the certification.
• Candidates will collaborate with data scientists, solution architects, AI developers, IoT specialists, and data engineers to turn their vision into complete AI solutions.
• By earning the AI-102 certification, you can demonstrate to employers your capability to develop AI solutions on Azure.
• This will qualify you with the skills to land high-paying positions as a Microsoft-certified Azure AI Engineer Associate.
• The estimated total pay for a Ai Engineer is $2,02,228 per year in the Us area, with an average salary of $1,31,836 per year. 

AI-102 Azure Exam Skills

• Plan and manage an Azure AI solution (15–20%)
  • Select the appropriate Azure AI service
    • Select the proper service for a computer vision solution
    • Choose the right service provider to implement natural language processing.
    • Find a suitable conversation solution provider from the list of options.
    • Select the right provider for a generative AI solution.
    • Find the appropriate service for a document intelligence solution
    • Choose the right service, to implement a knowledge-mining solution
  • Plan, create and deploy an Azure AI service
    • Plan for a solution that meets Responsible AI principles
    • Create an Azure AI resource
    • Determine a default endpoint for a service
    • Integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
    • Plan and implement a container deployment.
  • Manage, monitor, and secure an Azure AI service
    •  Configure diagnostic logging
    • Monitor an Azure AI resource
    • Manage costs for Azure AI services
    • Manage account keys
    • Protect account keys by using Azure Key Vault
    • Manage authentication for an Azure AI Service resource
    • Manage private communications.
• Implement content moderation solutions (10–15%)
  • Create solutions for content delivery
    • Implement a text moderation solution with Azure AI Content Safety
    • Implement an image moderation solution with Azure AI Content Safety
• Implement computer vision solutions (15–20%)
  • Analyze images
    • Select visual features to meet image processing requirements
    • Detect objects in images and generate image tags
    • Include image analysis features in an image processing request
    • Interpret image processing responses
    • Extract text from images using Azure AI Vision
    • Convert handwritten text using Azure AI Vision
  • Implement custom computer vision models by using Azure AI Vision
    • Choose between image classification and object detection models
    • Label images
    • Train a custom image model, including image classification and object detection
    • Evaluate custom vision model metrics
    • Publish a custom vision model
    • Consume a custom vision model
  • Analyze videos
    • Use Azure AI Video Indexer to extract insights from a video or livestream
    • Use Azure AI Vision Spatial Analysis to detect the presence and movement of people in the video.
• Implement natural language processing solutions (30–35%)
  • Analyze text by using Azure AI-Language
    •  Extract key phrases
    • Extract entities
    • Determine the sentiment of the text
    • Detect the language used in the text
    • Detect personally identifiable information (PII) in text.
  • Process speech by using Azure AI Speech
    • Implement text-to-speech
    • Implement speech-to-text
    • Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
    • Implement custom speech solutions
    • Implement intent recognition
    • Implement keyword recognition
  • Translate language
    • Translate text and documents by using the Azure AI Translator service
    • Implement custom translation, including training, improving, and publishing a custom model
    • Translate speech-to-speech by using the Azure AI Speech service
    • Translate speech-to-text by using the Azure AI Speech service
    • Translate to multiple languages simultaneously.
  • Implement and manage a language understanding model by using Azure AI-Language
    • Create intents and add utterances
    • Create entities
    • Train, evaluate, deploy, and test a language understanding model
    • Optimize a language understanding model
    • Consume a language model from a client application
    • Backup and recover language understanding models.
  • Create a custom question-answering solution by using Azure AI-Language
    • Create a custom question-answering project
    • Add question-and-answer pairs manually
    • Import sources
    • Train and test a knowledge base
    • Publish a knowledge base
    • Create a multi-turn conversation
    • Add alternate phrasing
    • Add chit-chat to a knowledge base
    • Export a knowledge base
    • Create a multi-language question-answering solution.
• Implement knowledge mining and document intelligence solutions (10–15%)
  • Implement an Azure AI Search solution
    •  Provision an Azure AI Search resource
    • Create data sources
    • Create an index
    • Define a skillset
    • Implement custom skills and include them in a skillset
    • Create and run an indexer
    • Query an index, including syntax, sorting, filtering, and wildcards
    • Manage Knowledge Store projections, including file, object, and table projections.
  • Implement an Azure AI Document Intelligence solution
    • Provision a Document Intelligence resource
    • Use prebuilt models to extract data from documents
    • Implement a custom document intelligence model
    • Train, test, and publish a custom document intelligence model
    • Create a composed document intelligence model
    • Implement a document intelligence model as a custom Azure AI Search skill.
• Implement generative AI solutions (10–15%)
  • Use Azure OpenAI Service to generate content
    • Provision an Azure OpenAI Service resource
    • Select and deploy an Azure OpenAI model
    • Submit prompts to generate natural language
    • Submit prompts to generate code
    • Use the DALL-E model to generate images
    • Use Azure OpenAI APIs to submit prompts and receive responses.
  • Optimize generative AI
    • Configure parameters to control generative behavior
    • Apply prompt engineering techniques to improve responses
    • Use your data with an Azure OpenAI model
    • Fine-tune an Azure OpenAI model.

What are the prerequisites of the AI-102 Exam?

There are no formal prerequisites, it’s advisable to have experience with Azure AI services and a solid understanding of programming languages.

• Proficiency in programming languages like Python, C#, JavaScript or Java.
• Experience with REST-based APIs and SDKs to build AI solutions.

Exam Details Microsoft Azure AI Engineer Certification

How to Prepare for the AI-102 Exam?

• Familiarize Yourself with AI Concepts: Understand basic AI principles, including machine learning, natural language processing, and computer vision. Knowledge of any programming language like Python, C# or javascript.
• Explore Azure Services: Understand Azure Cognitive Services, Azure Machine Learning, and the Microsoft Bot Framework.
• Study the Exam Objectives: Review the skills measured in the exam, such as implementing AI solutions, integrating AI with applications, and managing AI models. Microsoft Azure Guide
• Hands-On Practice: Engage in hands-on labs and projects to apply your knowledge using Azure tools and services.
• Utilize Learning Resources: Take advantage of official Microsoft learning paths, online courses, books, and documentation to deepen your understanding .
• Join Study Groups or Communities: Participate in online forums or study groups to share knowledge and resources with peers.
• Take Practice Exams: Test your knowledge with practice exams to identify areas where you need improvement.
• Prepare for Exam Day: Review test-taking strategies and ensure you understand the exam format and requirements before the exam day.
• Stay Updated: Keep up-to-date with the latest trends and updates in AI and Azure technologies, as these fields are constantly evolving.

Who Should Get AI102 Certification?

1. AI Engineers: AI 102 designing and implementing a Microsoft Azure AI solution using Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.
2. Developers: Those who build and integrate AI models into applications and services.
3. Data Scientists: Individuals who want to enhance their skills in deploying AI models and solutions on Azure.
4. Solution Architects: Professionals responsible for designing AI solutions that meet business requirements.
5. IT Professionals: Those looking to specialize in AI and machine learning within the Azure ecosystem.
6. Students and New Graduates: Individuals aiming to start a career in AI and cloud computing with a recognized certification.

Frequently Asked Questions

What is the passing score for the AI102 certification exam?

A score of 700 or greater is required to pass.

Are the AI-102 exam objectives subject to change?

Yes, the exam objectives can be updated periodically to reflect the latest skills and technologies. It’s important to check the most current exam guide before taking the test.

Is hands-on experience necessary to pass the AI102 exam?

Hands-on experience is highly recommended, as the exam includes practical tasks that require familiarity with Azure AI services.

Is the AI 102 exam hard?

The AI-102 exam is tough, but with good preparation, you can pass it. You can try AI-102 practice test to boost your confidence.

What is difference between AI-100 and AI-102?

AI-102 replaced AI-100 and focuses more on AI software engineering rather than solution architecture. The new exam Ai-102 designing and implementing a Microsoft Azure AI solution tests your ability to use Azure AI services.

Should I take AI 900 before AI 102?

AI-900 before AI-102 can be helpful as it provides a foundational understanding of AI concepts and Azure AI services, making the more advanced AI-102 exam easier to handle. However, it’s not mandatory, so if you already have a good hold of the basics, you can go straight to AI-102.

What is the exam Retake Policy?

You can retake the exam after 24 hours for the first retake. If you need to retake it, you must wait 14 days between attempts. You can take the exam up to five times a year.
Remember, many people don’t pass on their first attempt. Use this experience as a learning opportunity to better prepare for your next try.

Time to level up!
All the Best!