AI-102 Exam Study Guide: Azure AI Engineer Associate Certification

Updated – 20/07/2023 – The exam study guide below includes a new Free practice assessment for the AI-102 certification.

Updated – 20/07/2023 – The exam guide below shows the changes that will be implemented starting on May 2, 2023. The study guide has been updated to reflect the new objectives and exam topic weights added and removed by Microsoft Learning.

In this article, we will share with you how to prepare and pass the AI-102 exam, Azure AI Engineer Associate Certification successfully.

Introduction

As everyone is exploring Artificial Intelligence and looking to increase their skills, I thought I would try and lend a hand and create a study guide for the AI-102 exam that gets you the Azure AI Engineer Associate certification.

Microsoft is keeping evolving its e-learning programs to help you and your career keep pace with today’s demanding IT environments. The new updated role-based certifications will help you to keep pace with today’s business requirements. Microsoft Learning is constantly evolving its e-learning program to better offer what you need to skill up, prove your expertise to employers and peers, and get the recognition—and opportunities you’ve earned.

In February 2021, Microsoft announced a new certification exam that focus on Azure AI solutions which replace Exam AI-100 (retired on June 30, 2021). The Exam AI-102 is the only exam required to earn an Azure AI Engineer Associate certification.

Exam Target Audience

The role of Microsoft Azure AI engineers entails build, administration, and implementation of AI solutions that leverage the capabilities of Azure Cognitive Services and Azure services. Their responsibilities encompass all stages of AI solution development, including requirement gathering, design, development, deployment, integration, maintenance, performance optimization, and monitoring.

These professionals collaborate closely with solution architects to translate their vision into reality. They also work alongside data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to construct comprehensive end-to-end AI solutions.

Azure AI engineers possess expertise in utilizing programming languages such as Python or C# and are adept at leveraging REST-based APIs and software development kits (SDKs) to construct secure solutions for image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI on the Azure platform. They are well-versed in various methods of implementing AI solutions and possess a comprehensive understanding of the components that constitute the Azure AI portfolio, as well as the available data storage options. Furthermore, Azure AI engineers demonstrate a deep understanding of responsible AI principles and are proficient in their applications.

Importance of AI-102 Certification

The Microsoft AI-102 certification holds paramount importance for professionals seeking to enrich their knowledge and proficiency in artificial intelligence while advancing their careers.

This certification validates candidates’ skills in designing and implementing AI solutions using Microsoft technologies and tools. Globally recognized, the certification showcases to employers and clients that the certified expert possesses the ability and knowledge to develop and deploy AI solutions using Azure services.

Obtaining the certification can boost the professional’s credibility, opening doors to better job opportunities and higher salaries. The certification program necessitates learning about the latest tools and technologies in AI development, empowering candidates to stay ahead of the curve and remain competitive in the job market.

Keeping up with current developments and trends in AI is crucial, and the certification facilitates professionals in achieving this goal. The certification offers a structured learning path for those interested in AI, serving as a benchmark for measuring their progress and skill level. Designed to cater to both technical and business professionals, this certification is a versatile and valuable accreditation to pursue.

AI-102 Prerequisites Knowledge

To successfully benefit from this certification, you must have a working knowledge of the Microsoft Azure platform. Please note that there is no content around Azure administration tasks or how to navigate the Azure platform. You are also expected to have completed Azure Fundamentals and Azure AI Fundamentals training, or equivalent before undertaking this certification.

Also, the following are other areas that are considered critical knowledge to succeed in this exam:

• Familiarity with Azure and the Azure portal.
• Familiarity with Visual Studio Code.
• Familiarity with C# and Python.
• Familiarity with REST programming principles and JSON.
• Familiarity with Azure Cognitive Services, Azure Bot Service, and Azure Cognitive Search.

If you have no previous programming experience, it’s recommended to complete the Take your first steps with C# or Take your first steps with Python learning path before starting the AI-102 exam preparation.

AI-102 Exam Preparation

How do you prepare for the AI-102 exam?

I would like to share with you how to prepare and pass the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam successfully to obtain the Azure AI Engineer Associate certification.

Azure OpenAI Service provides access to OpenAI’s powerful large language models such as ChatGPT, GPT, Codex, and Embeddings models. These models enable various natural language processing (NLP) solutions to understand, converse, and generate content. Users can access the service through REST APIs, SDKs, and Azure OpenAI Studio.

Skills measured on the AI-102 exam

This exam measures your ability to accomplish the technical topics listed below based on the latest update from Microsoft.

Links to relevant reading from the official Microsoft Learn documentation for each skill tested are listed below to help you prepare for this exam:

Plan and manage an Azure AI solution (25–30%)

Select the appropriate Azure AI service

• Select the appropriate service for a vision solution
• Select the appropriate service for a language analysis solution
• Select the appropriate service for a decision-support solution
• Select the appropriate service for a speech solution
• Select the appropriate Applied AI services

Plan and configure security for Azure AI services

• Manage account keys
• Manage authentication for a resource
• Secure services by using Azure Virtual Networks
• Plan for a solution that meets Responsible AI principles

Create and manage an Azure AI service

• Create an Azure AI resource
• Configure diagnostic logging
• Manage costs for Azure AI services
• Monitor an Azure AI resource

Deploy Azure AI services

• Determine a default endpoint for a service
• Create a resource by using the Azure portal
• Integrate Azure AI services into a continuous integration/continuous deployment (CI/CD) pipeline
• Plan a container deployment
• Implement prebuilt containers in a connected environment

Create solutions to detect anomalies and improve the content

• Create a solution that uses an Anomaly Detector, part of Cognitive Services
• Create a solution that uses Azure Content Moderator, part of Cognitive Services
• Create a solution that uses Personalizer, part of Cognitive Services
• Create a solution that uses Azure Metrics Advisor, part of Azure Applied AI Services
• Create a solution that uses Azure Immersive Reader, part of Azure Applied AI Services

Learning Path

• Prepare for AI engineering (1 module)
• Provision and manage Azure Cognitive Services (4 modules)
• Process and translate Text with Azure Cognitive Services (2 modules)
• Process and Translate Speech with Azure Cognitive Speech Services (2 modules)

Implement image and video processing solutions (15–20%)

Analyze images

• Select appropriate visual features to meet image processing requirements
• Create an image processing request to include appropriate image analysis features
• Interpret image processing responses

Extract text from images

• Extract text from images or PDFs by using the Computer Vision service
• Convert handwritten text by using the Computer Vision service
• Extract information using prebuilt models in Azure Form Recognizer
• Build and optimize a custom model for Azure Form Recognizer

Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services

• Choose between image classification and object detection models
• Specify model configuration options, including category, version, and compact
• Label images
• Train custom image models, including classifiers and detectors
• Manage training iterations
• Evaluate model metrics
• Publish a trained iteration of a model
• Export a model to run on a specific target
• Implement a Custom Vision model as a Docker container
• Interpret model responses

Process videos

• Process a video by using Azure Video Indexer
• Extract insights from a video or live stream by using Azure Video Indexer
• Implement content moderation by using Azure Video Indexer
• Integrate a custom language model into Azure Video Indexer

Learning Path

• Extract text from images and documents (2 modules)
• Create computer vision solutions with Azure Cognitive Services (5 modules)

Implement natural language processing solutions (25–30%)

Analyze text

• Retrieve and process key phrases
• Retrieve and process entities
• Retrieve and process sentiment
• Detect the language used in the text
• Detect personally identifiable information (PII)

Process speech

• Implement and customize text-to-speech
• Implement and customize speech-to-text
• Improve text-to-speech by using SSML and Custom Neural Voice
• Improve speech-to-text by using phrase lists and Custom Speech
• Implement intent recognition
• Implement keyword recognition

Translate language

• Translate text and documents by using the Translator service
• Implement custom translation, including training, improving, and publishing a custom model
• Translate speech-to-speech by using the Speech service
• Translate speech-to-text by using the Speech service
• Translate to multiple languages simultaneously

Build and manage a language understanding model

• Create intents and add utterances
• Create entities
• Train evaluate, deploy, and test a language understanding model
• Optimize a Language Understanding (LUIS) model
• Integrate multiple language service models by using Orchestrator
• Import and export language understanding models

Create a question-answering solution

• Create a 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
• Create a multi-domain question-answering solution
• Use metadata for question-and-answer pairs

Learning Path

• Build a question-answering solution (1 module)
• Create a Language Understanding solution with Azure Cognitive Services (2 modules)

Implement knowledge mining solutions (5–10%)

Implement a Cognitive Search Solution

• Provision a Cognitive Search resource
• Create data sources
• Define an index
• Create and run an indexer
• Query an index, including syntax, sorting, filtering, and wildcards
• Manage knowledge store projections, including file, object, and table projections

Apply AI enrichment skills to an indexer pipeline

• Attach a Cognitive Services account to a skillset
• Select and include built-in skills for documents
• Implement custom skills and include them in a skillset
• Implement incremental enrichment

Learning Path

• Implement knowledge mining with Azure Cognitive Search (8 modules)

Implement conversational AI solutions (15–20%)

Design and implement conversation flow

• Design conversational logic for a bot
• Choose appropriate activity handlers, dialogs or topics, triggers, and state handling for a bot

Build a conversational bot

• Create a bot from a template
• Create a bot from scratch
• Implement activity handlers, dialogs or topics, and triggers
• Implement channel-specific logic
• Implement Adaptive Cards
• Implement multi-language support in a bot
• Implement multi-step conversations
• Manage state for a bot
• Integrate Cognitive Services into a bot, including question answering, language understanding, and Speech service

Test, publish, and maintain a conversational bot

• Test a bot using the Bot Framework Emulator or the Power Virtual Agents web app
• Test a bot in a channel-specific environment
• Troubleshoot a conversational bot
• Deploy bot logic

Learning Path

• Create conversational AI solutions (2 modules)
• Develop Generative AI solutions with Azure OpenAI Service (7 modules)

AI-102 Training Labs

These hands-on lab exercises support the official Microsoft course AI-102 Designing and Implementing a Microsoft Azure AI Solution and the equivalent self-paced modules on Microsoft Learn. The exercises are designed to accompany the learning materials and enable you to practice using the technologies they describe.

To complete these exercises, please note that you’ll require a Microsoft Azure subscription. You can sign up for a free trial at https://azure.microsoft.com.

AI Engineer Exercises

• Lab Environment Setup

Exercises

• AI 00: Enable Resource Providers
• AI 01: Get Started with Cognitive Services
• AI 02: Manage Cognitive Services Security
• AI 03: Monitor Cognitive Services
• AI 04: Use a Cognitive Services Container

• AI 05: Analyze Text
• AI 06: Translate Text
• AI 07: Recognize and Synthesize Speech
• AI 08: Translate Speech
• AI 09: Create a language understanding model with the Language service

• AI 10: Create a Conversational Language Understanding Client Application
• AI 11: Create a Question Answering Solution
• AI 11: Create a Bot with the Bot Framework SDK
• AI 13: Create a Bot with Bot Framework Composer
• AI 13: Analyze Images with Computer Vision

• AI 14: Analyze Video with Video Analyzer
• AI 15: Classify Images with Custom Vision
• AI 16: Detect Objects in Images with Custom Vision
• AI 17: Detect and Analyze Faces
• AI 18: Read Text in Images

• AI 19: Extract Data from Forms
• AI 20: Create an Azure Cognitive Search Solution
• AI 21: Create a Custom Skill for Azure Cognitive Search
• AI 22: Create a Knowledge Store with Azure Cognitive Search

AI-102 Video Training

If  you are interested to prepare for this exam using video training, then I highly recommend checking the following resources:

> Pluralsight: If you have access to a Pluralsight Learning platform, then I highly recommend going through the following exam preparation learning path: Microsoft Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution (10 hours).

> YouTube: AI-102 Study Cram – Azure AI Engineer Associate Certification by John Savill.

> Udemy: AI-102 Microsoft Azure AI Solution Complete Exam Prep by Scott Duffy.

AI-102 Exam Readiness

The Microsoft Learn exam readiness zone is a team of experts who share valuable insights, techniques, and strategies to help you prepare effectively for your Microsoft Certification exam.

The comprehensive exam preparation videos will assist you in identifying the key areas of knowledge and skills assessed in the AI-102 exam, as well as guide you on how to allocate your study time efficiently. Each video segment below corresponds to a significant topic covered in the latest AI-102 exam, and the trainer highlights objectives that many test takers typically find challenging.

1) Preparing for AI-102 – Plan and manage an Azure AI solution.

2) Preparing for AI-102 – Implement image and video processing solutions.

3) Preparing for AI-102 – Implement natural language processing solutions.

4) Preparing for AI-102 – Implement knowledge mining solutions.

5) Preparing for AI-102 – Implement conversational AI solutions.

These free videos include illustrative sample questions and detailed explanations of the answers. We recommend watching these videos once you have completed your training or gained some practice, although you are welcome to view them at any stage of your certification journey. Additionally, they provide additional resources to further aid you in your AI-102 exam preparation.

AI-102 Practice Test

If you wish to validate your skills before taking the real exam, I highly encourage you to purchase the following practice test:

AI-102: Designing and Implementing an Azure AI Solution Microsoft Certification Practice Test. The MeasureUp AI-102: Designing and Implementing a Microsoft Azure AI Solution practice test from mind hub is designed to help you prepare for and pass the Microsoft AI-102 exam. This exam is aimed at candidates who want to validate their skills.

You should know C# or Python and be able to use REST-based APIs and SDKs. And you should be able to participate in all phases of AI solutions development, as well as to work with solution architects to translate their vision and to work with other experts to build complete end-to-end AI solutions.

The AI-102 test contains 144 questions and covers the following objectives:

• Plan and manage an Azure Cognitive Services solution – 23 questions.
• Implement Computer Vision solutions – 36 questions.
• Implement natural language processing solutions – 29 questions.
• Implement knowledge mining solutions – 25 questions.
• Implement conversational AI solutions – 31 questions.

AI-102 Free Practice Assessment

Are you preparing for the AI-102 certification exam? Microsoft just announced Practice Assessments on Microsoft Learn, the newest free exam preparation resource that allows you to assess your knowledge and fill knowledge gaps so that you are better prepared the take the AI-102 certification exam.

The following assessment provides you with an overview of the style, wording, and difficulty of the questions you’re likely to experience on the exam. Through this assessment, you’re able to assess your readiness, determine where additional preparation is needed, and fill knowledge gaps bringing you one step closer to the likelihood of passing your AI-102 exam.

> Take now the Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution Practice Assessment (50 questions).

Prepare for your certification exam by assessing your knowledge through Practice Assessments, which are free and can be attempted multiple times. These assessments are created and regularly updated by the same team that develops the official certification exams.

You can access practice assessments on Microsoft Learn by signing in or creating an account. The score report for each question includes the answer, rationale, and links to additional information.

Schedule AI-102 Exam

Once you are ready, click Schedule exam here and take it online from the comfort of your home/office with proctor supervision.

If you are planning to take the AI-102 exam… I wish you all the best and Happy Studying!!!

AI-102 Exam FAQs

What is the passing score for the AI-102 exam?

The passing score for the AI-102 exam is 700 out of 1000 points.

How many questions are there in the AI-102 exam?

The AI-102 exam consists of approximately 40-60 questions, and the total time for the AI-102 exam is 130 minutes (2.10 hours)

Are there any prerequisites for the Azure AI Engineer Associate Certification?

Yes, you should have some prior experience with Azure services and basic knowledge of AI concepts.

Can I retake the AI-102 exam if I fail?

Yes, you can retake the exam after waiting for 24 hours from your first attempt.

Are the AI-102 exam objectives subject to change?

Yes, Microsoft may update the exam objectives to align with the evolving industry trends.

Is hands-on experience necessary for passing the AI-102 exam?

Hands-on experience with Azure AI services and tools will significantly improve your chances of passing the exam.

Conclusion

The Microsoft AI-102 certification exam holds great significance for professionals aiming to demonstrate their proficiency in designing and implementing Azure AI solutions. It presents a great challenge, demanding a profound grasp of Azure services, AI technologies, and data science principles. Nevertheless, you can enhance the likelihood of success by leveraging appropriate study materials, resources, and strategic approaches shared in this article.

Attaining the Microsoft AI-102 certification enables professionals to propel their careers forward, showcase their expertise to employers and clients, and gain a competitive edge in the job market.

Other Microsoft Azure Exam Study Guides

Are you interested in another Azure certification exam? I highly encourage you to check out the following Azure exam study guides:

• Exam AI-900: Microsoft Azure AI Fundamentals Exam Study Guide
• Exam AZ-900: Microsoft Azure Fundamentals Exam Study Guide
• Exam AZ-104: Microsoft Azure Administrator Exam Study Guide
• Exam AZ-140: Microsoft Azure Virtual Desktop Exam Study Guide
• Exam AZ-204: Developing Solutions for Microsoft Azure Exam Study Guide
• Exam AZ-305: Designing Microsoft Azure Infrastructure Solutions Study Guide
• Exam AZ-500: Microsoft Azure Security Technologies Exam Study Guide
• Exam AZ-700: Microsoft Azure Network Engineer Associate Study Guide
• Exam SC-900: Microsoft Security, Compliance, and Identity Fundamentals Exam Study Guide
• Exam SC-200: Microsoft Security Operations Analyst Exam Study Guide
• Exam SC-300: Microsoft Identity and Access Administrator Exam Study Guide
• Exam SC-400: Microsoft Information Protection Administrator Exam Study Guide
• Exam SC-100: Microsoft Cybersecurity Architect Exam Study Guide
• Exam AZ-800: Administering Windows Server Hybrid Core Infrastructure Study Guide
• Exam AZ-801: Configuring Windows Server Hybrid Advanced Services Study Guide

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-Charbel Nemnom-