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AI-102 Microsoft Azure AI Solution Complete Exam Prep 2024

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Based on 3,738 ratings
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Scott Duffy • 1.000.000+ Students and Software Architect.ca

Covers the requirements of the AI-102 Exam, Designing and Implementing a Microsoft Azure AI Solution.

Course updated continuously since launch, adding new quizzes and resources.

Course completely re-recorded in APRIL 2024. Up-to-date with the latest exam requirements.

Candidates for Exam AI-102 should have subject-matter expertise in building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Read more

Covers the requirements of the AI-102 Exam, Designing and Implementing a Microsoft Azure AI Solution.

Course updated continuously since launch, adding new quizzes and resources.

Course completely re-recorded in APRIL 2024. Up-to-date with the latest exam requirements.

Candidates for Exam AI-102 should have subject-matter expertise in building, managing, and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework.

Candidates for this exam should be proficient in C# or Python and should be able to use REST-based APIs and SDKs to build computer vision, natural language processing, knowledge mining, and conversational AI solutions on Azure. They should also understand the components that make up the Azure AI portfolio and the available data storage options. Plus, candidates need to understand and be able to apply responsible AI principles.

This exam measures your ability to accomplish the following technical tasks: plan and manage an Azure AI solution, Implement content moderation solutions, Implement computer vision solutions, Implement natural language processing solutions, Implement knowledge mining and document intelligence solutions, and Implement generative AI solutions.

  • Plan and manage an Azure AI solution (15–20%)

  • Implement content moderation solutions (10–15%)

  • Implement computer vision solutions (15–20%)

  • Implement natural language processing solutions (30–35%)

  • Implement knowledge mining and document intelligence solutions (10–15%)

  • Implement generative AI solutions (10–15%)

Taught by Scott Duffy, the number one instructor of Microsoft Azure on Udemy.

Microsoft, Windows, and Microsoft Azure are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries. This course is not certified, accredited, affiliated with, nor endorsed by Microsoft Corporation.

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What's inside

Learning objectives

  • Know how to implement solutions for the microsoft azure ai platform
  • Pass the microsoft ai-102 microsoft azure ai engineer test the first time
  • Achieve the azure ai engineer associate badge
  • Understand the main concepts of azure ai services, beyond the ones you normally use
  • Be up-to-date on the latest updates to this ever-changing platform

Syllabus

Welcome to the Course
Hello and Welcome to AI-102
Exam Requirements for AI-102
Quick Demo: Using Cognitive Services for Language (was Text Analytics) in .NET
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers the requirements of the AI-102 exam, which is helpful for those seeking to validate their skills in designing and implementing Microsoft Azure AI solutions
Proficiency in C# or Python is needed, so learners can leverage REST-based APIs and SDKs to build computer vision and natural language processing solutions
Teaches how to implement content moderation solutions, which is useful for those working with user-generated content or needing to comply with content policies
Explores generative AI solutions, which are increasingly important for creating new content and automating tasks in various industries
Requires familiarity with REST-based APIs and SDKs, which may pose a challenge for learners without prior experience in software development or web services
Updated in April 2024, which ensures that the content aligns with the latest exam requirements and industry best practices

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.
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Reviews summary

Comprehensive ai-102 azure exam prep

According to learners, this course offers solid preparation for the AI-102 Microsoft Azure AI Solution exam. Many students highlight the clarity of the instructor's explanations and find the hands-on labs to be particularly useful for understanding practical applications of Azure AI services like Cognitive Services and Cognitive Search. While older reviews sometimes noted content being slightly outdated, recent feedback following the April 2024 re-recording indicates significant improvements in currency. Learners suggest that supplementing the course material with official Microsoft documentation can provide deeper dives into certain topics and ensure the most comprehensive exam coverage.
Official docs are recommended.
"Good starting point, but official Microsoft Learn is needed for full depth."
"I recommend using this course alongside Microsoft's documentation."
"Found I needed to supplement with additional reading for some topics."
Recent update improved currency.
"The April 2024 update really brought the content current."
"Much better now that the course has been re-recorded."
"Seems mostly current with the latest Azure portal and services."
Explanations are clear and easy to follow.
"The instructor explains complex topics very clearly."
"Scott's teaching style is engaging and easy to understand."
"Appreciated the way the concepts were broken down simply."
Practical exercises aid understanding.
"The labs were incredibly helpful for reinforcing concepts."
"I learned the most from doing the actual labs and demos."
"Provides good hands-on practice that is essential for Azure services."
Covers necessary AI-102 exam topics.
"Felt well-prepared for the exam after taking this course."
"The course structure aligns well with the AI-102 syllabus."
"Covers all the major areas required for the certification."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI-102 Microsoft Azure AI Solution Complete Exam Prep 2024 with these activities:
Review Core Python Concepts
Solidify your Python foundation before diving into Azure AI SDKs. Refreshing your knowledge of Python syntax, data structures, and control flow will make it easier to understand and implement the code examples in the course.
Browse courses on Python Basics
Show steps
  • Review Python syntax and data types.
  • Practice writing basic Python functions.
  • Work through online Python tutorials.
Review REST API Concepts
Familiarize yourself with REST API concepts to effectively interact with Azure Cognitive Services. Understanding how to make requests, handle responses, and authenticate will be crucial for implementing AI solutions.
Show steps
  • Study REST API architecture and methods.
  • Practice making API calls using tools like Postman.
  • Review API authentication methods.
Follow Azure AI Vision Tutorials
Deepen your understanding of computer vision by working through practical tutorials. Following step-by-step guides will help you apply the concepts learned in the course and build confidence in your ability to implement vision solutions.
Show steps
  • Find tutorials on the Microsoft Azure documentation site.
  • Implement the tutorial examples using the Azure portal or SDK.
  • Experiment with different parameters and settings.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Build a Simple Image Classifier
Solidify your knowledge of Custom Vision by building a practical image classification project. This hands-on experience will reinforce the concepts learned in the course and allow you to apply them to a real-world scenario.
Show steps
  • Gather a dataset of images for your chosen classification task.
  • Create a Custom Vision project in Azure.
  • Train and evaluate your image classification model.
  • Deploy your model and test its performance.
Write a Blog Post on Azure AI Services
Reinforce your understanding of Azure AI services by writing a blog post explaining their capabilities and use cases. Explaining concepts in your own words will help solidify your knowledge and improve your communication skills.
Show steps
  • Choose a specific Azure AI service to focus on.
  • Research the service's features, benefits, and limitations.
  • Write a clear and concise blog post explaining the service.
  • Include code examples or screenshots to illustrate your points.
Contribute to an Open Source AI Project
Deepen your understanding of AI and Azure by contributing to an open-source project. Working with real-world codebases and collaborating with other developers will provide valuable experience and enhance your skills.
Show steps
  • Find an open-source AI project on GitHub or GitLab.
  • Identify an issue or feature that you can contribute to.
  • Submit a pull request with your changes.
  • Respond to feedback from other contributors.
Read 'Programming Microsoft Azure AI Services'
Supplement your learning with a comprehensive guide to programming Azure AI Services. This book provides in-depth explanations and practical examples that will help you master the concepts covered in the course.
View Putting Knowledge to Work on Amazon
Show steps
  • Read the chapters relevant to the course topics.
  • Work through the code examples in the book.
  • Experiment with different parameters and settings.

Career center

Learners who complete AI-102 Microsoft Azure AI Solution Complete Exam Prep 2024 will develop knowledge and skills that may be useful to these careers:
Artificial Intelligence Engineer
An Artificial Intelligence Engineer designs, develops, and implements AI solutions, and this course is directly aligned with those responsibilities using Microsoft Azure. This role involves working with various AI services, including computer vision, natural language processing, and knowledge mining, all extensively covered in this course. The course helps build a foundation for managing Azure AI solutions, implementing content moderation, and deploying generative AI models, all of which are core tasks for an Artificial Intelligence Engineer. The comprehensive coverage of the Azure AI platform makes this course particularly helpful for someone looking to enter this role.
See salaries and explore the career path for Artificial Intelligence Engineer
Computer Vision Engineer
A Computer Vision Engineer specializes in developing systems that can interpret and understand visual data, and this course provides a concentration of training in this area within Azure. This role requires a strong grasp of computer vision services and techniques, which are a strong focus of the course. The course is helpful in learning to implement different computer vision models for tasks like image tagging, object detection, facial recognition, and form detection. This course’s emphasis on using Azure Cognitive Services and the Python SDK will also be particularly relevant for this role. The course is essential for anyone focused on this particular area of AI.
See salaries and explore the career path for Computer Vision Engineer
Natural Language Processing Engineer
A Natural Language Processing Engineer develops systems that can process and understand human language, and this course helps build essential skills for this within the Azure AI Ecosystem. This role requires work with various language services, including entity recognition, sentiment analysis, and language translation, which are all covered in the course. The course also covers other important aspects such as speech-to-text and text-to-speech functionalities, which greatly expand the possibilities of this role. The focus on Microsoft’s Azure Cognitive Services for language makes this course particularly applicable to this role.
See salaries and explore the career path for Natural Language Processing Engineer
Chatbot Developer
A Chatbot Developer specializes in creating conversational AI applications, and this course is extremely relevant for that role with its training on Microsoft's Bot Framework and Language Understanding Services. This role requires a good understanding of natural language processing, which is a heavy focus in the course. The course also provides hands-on learning about using LUIS and QnA Maker to build intelligent conversational bots. The course’s comprehensive coverage of these technologies makes it especially valuable for those looking to become Chatbot Developers.
See salaries and explore the career path for Chatbot Developer
Machine Learning Engineer
A Machine Learning Engineer builds and deploys machine learning models, and this course provides a solid foundation by covering the implementation tasks within the Azure AI ecosystem. This role requires familiarity with computer vision, natural language processing, and knowledge mining and this course focuses on each of these areas using Azure Cognitive Services. This course also helps in understanding how to plan and manage Azure AI solutions, as well as implement generative AI models, which are essential for a Machine Learning Engineer. Furthermore, the course's emphasis on using REST APIs and SDKs align well with the job's technical requirements.
See salaries and explore the career path for Machine Learning Engineer
Azure Developer
An Azure Developer is tasked with building and deploying applications on the Azure platform, and this course will help them specialize in AI solutions that leverage Azure Cognitive Services. This role involves using tools like REST-based APIs and SDKs, which are extensively covered in the course. It provides the knowledge and skills to implement content moderation, computer vision, natural language processing, knowledge mining, and generative AI solutions, all of which are important in this role. The course’s focus on using C# and Python to build these solutions also aligns perfectly with the needs of an Azure Developer.
See salaries and explore the career path for Azure Developer
Software Engineer
A Software Engineer can greatly benefit from this course, especially if they are working on AI-driven applications, given that this course provides very focused training on building AI solutions within the Azure ecosystem. This role requires a strong understanding of APIs and SDKs, which are covered extensively in the course. This course is helpful because it teaches you how to implement computer vision, natural language processing, and knowledge mining solutions, which are frequently integrated into modern software. The use of C# or Python, as well as REST APIs, are all skills which are essential for a Software Engineer who wishes to build AI driven applications.
See salaries and explore the career path for Software Engineer
Technical Consultant
A Technical Consultant advises clients on technology solutions, and this course may be valuable to them given its focus on building AI solutions within the Microsoft Azure ecosystem. This course helps to build a broad understanding of Azure’s AI services, which are increasingly being integrated into many technology solutions. The course directly covers how to manage Azure AI solutions, implement content moderation, implement computer vision, how to implement natural language processing, and knowledge mining. This course’s practical focus on deploying AI makes it very useful to a technical consultant who might recommend use of Azure’s technology.
See salaries and explore the career path for Technical Consultant
AI Solutions Consultant
An AI Solutions Consultant advises clients on implementing AI solutions, and this course helps in developing a deep understanding of Azure AI services, which will be a valuable asset. The course focuses on computer vision, natural language processing, and knowledge mining, all of which are critical for creating effective solutions. This role also benefits from a broad understanding of the AI landscape, including responsible AI principles, which are covered in the course. Furthermore, the course provides hands-on experience in deploying various AI services, which can be crucial for offering practical recommendations to clients.
See salaries and explore the career path for AI Solutions Consultant
Knowledge Management Specialist
A Knowledge Management Specialist focuses on organizing and making information accessible within an organization. This course introduces the use of Azure Cognitive Search and other Cognitive Services for knowledge mining and document intelligence, which can help improve their work. This role involves creating systems that can efficiently organize and retrieve knowledge and this course helps to implement those systems using Azure. The course also provides hands on learning for creating question answering solutions, which is particularly useful for a Knowledge Management Specialist.
See salaries and explore the career path for Knowledge Management Specialist
Cloud Solutions Architect
A Cloud Solutions Architect designs cloud-based solutions, and this course may be useful for those focusing on AI solutions within the Microsoft Azure environment. This role requires a deep understanding of various Azure services, including AI services, data storage options, and security protocols. This course helps prepare someone for that by covering the planning and management of Azure AI solutions, including cost management and security configurations. The course may also provide insight into implementing computer vision, natural language processing, and knowledge mining solutions on Azure, which are growing areas of interest in cloud architecture.
See salaries and explore the career path for Cloud Solutions Architect
AI Product Manager
An AI Product Manager defines and guides the development of AI-driven products, and this course may be useful in gaining a practical understanding of the capabilities of Microsoft Azure AI. While they may not need to implement these solutions themselves, this course will help them understand the technical nuances of Azure AI functionality. This course covers the use of computer vision, natural language processing, and knowledge mining, which would inform product development strategy. The course will also provide insight into the AI landscape, which is important for an AI Product Manager.
See salaries and explore the career path for AI Product Manager
Data Scientist
A Data Scientist may find this course useful as it provides practical experience with Azure AI tools. While their role typically emphasizes statistical analysis and model development, using tools to deploy models is also essential, and this course touches on how to use Azure Cognitive Services and other AI tools. This course helps to better understand how to implement various data processing tasks using computer vision, natural language processing, and knowledge mining, which can be valuable for a Data Scientist. The course demonstrates how to deploy these solutions in a cloud environment, which is important when scaling their work.
See salaries and explore the career path for Data Scientist
AI Research Scientist
An AI Research Scientist might find this course useful for gaining an understanding of how AI models can be implemented using Azure cloud services. This role involves staying at the cutting edge of AI and machine learning, where understanding practical implementation can be very valuable. This course covers content moderation, computer vision, natural language processing, and knowledge mining within the Azure AI platform, which may supplement their research. While their focus is on theoretical advancements, understanding the possibilities of implementing solutions using Azure services will likely be beneficial.
See salaries and explore the career path for AI Research Scientist
Robotics Engineer
A Robotics Engineer may find that this course provides a useful introduction to some aspects of computer vision, a very important subfield in robotics. While the course does not directly address the more specific challenges of robotics, the section on computer vision can be a good introduction to how visual information can be interpreted, which is a core component of much work within robotics. This course may help them to understand how to implement models for image tagging, object detection, and facial recognition, all of which can inform their work. The course provides the ability to work with these technologies within the Microsoft Azure environment.
See salaries and explore the career path for Robotics Engineer

Reading list

We've selected one books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in AI-102 Microsoft Azure AI Solution Complete Exam Prep 2024.
Cover image
Putting Knowledge to Work
Save
Provides a comprehensive guide to using Azure AI Services with code examples in Python and C#. It covers a wide range of topics, including computer vision, natural language processing, and knowledge mining. This book is particularly useful for understanding the practical implementation of the concepts covered in the course. It serves as a valuable reference for building real-world AI solutions on Azure.
Putting Knowledge to Work
Paperback
Check
Putting Knowledge to Work
Kindle Edition
Check

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Effort
15.5 total hours
Via
Udemy
Instructors
Scott Duffy • 1.000.000+ Students
Software Architect.ca
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Covers the requirements of the AI-102 exam, which is helpful for those seeking to validate their skills in designing and implementing Microsoft Azure AI solutions
Proficiency in C# or Python is needed, so learners can leverage REST-based APIs and SDKs to build computer vision and natural language processing solutions
Teaches how to implement content moderation solutions, which is useful for those working with user-generated content or needing to comply with content policies
Explores generative AI solutions, which are increasingly important for creating new content and automating tasks in various industries
Requires familiarity with REST-based APIs and SDKs, which may pose a challenge for learners without prior experience in software development or web services
Updated in April 2024, which ensures that the content aligns with the latest exam requirements and industry best practices
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