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Microsoft Azure AI Engineer (AI-102) Course

Item: certified-azure-ai-engineer-ai-102-course
Rating: 10
Author: Johan Andersson

Become an AI expert on Azure. Learn to design and implement AI solutions for optimal business transformation and innovation.

course: Microsoft Certified Azure AI Engineer (AI-102)

Varighed: 5 days

Format: Virtual or Classroom

Prepares for Exam: Designing and Implementing a Microsoft Azure AI Solution AI-102

Prepares for Certification: Microsoft Certified: Azure AI Engineer Associate

Attend this and 60+ other Microsoft courses for FREE with Unlimited Microsoft Training

Overview

The Microsoft Certified Azure AI Engineer (AI-102) course teaches you how to design, build, and deploy AI solutions on Microsoft Azure. You will work with cognitive services, conversational AI, and Azure AI tools to integrate intelligent features into business applications. This instructor-led training prepares you for the AI-102 exam and the Microsoft Certified: Azure AI Engineer Associate certification.

This course includes

Instructor-led training
Practice test
Pre-reading
Personal Learning Path
Certification Guarantee
Email, chat and phone support

Who is the Microsoft Certified Azure AI Engineer (AI-102) training course for?

The Microsoft Certified Azure AI Engineer (AI-102) certification is for individuals with experience in designing and implementing AI solutions using Azure tools and services. The certification validates the ability to use Azure Cognitive Services, Machine Learning, and other AI technologies to build intelligent solutions. To obtain the certification, candidates must pass the AI-102 exam, which covers topics such as designing and implementing AI solutions, working with data storage and processing, building and deploying models using Azure Machine Learning, and natural language processing and computer vision technologies.

Curriculum

What you will learn during our Microsoft Certified Azure AI Engineer course.

Plan and Manage an Azure Cognitive Services Solution

Select the appropriate Cognitive Services resource
Select the appropriate cognitive service for a vision solution
Select the appropriate cognitive service for a language analysis solution
Select the appropriate cognitive Service for a decision support solution
Select the appropriate cognitive service for a speech solution

Plan and configure security for a Cognitive Services solution

Manage Cognitive Services account keys
Manage authentication for a resource
Secure Cognitive Services by using Azure Virtual Network
Plan for a solution that meets responsible AI principles

Create a Cognitive Services resource

Create a Cognitive Services resource
Configure diagnostic logging for a Cognitive Services resource
Manage Cognitive Services costs
Monitor a cognitive service
Implement a privacy policy in Cognitive Services

Plan and implement Cognitive Services containers

Identify when to deploy to a container
Containerize Cognitive Services (including Computer Vision API, Face API, Text Analytics, Speech, Form Recognizer)

Analyze images by using the Computer Vision API

Retrieve image descriptions and tags by using the Computer Vision API
Identify landmarks and celebrities by using the Computer Vision API
Detect brands in images by using the Computer Vision API
Moderate content in images by using the Computer Vision API
Generate thumbnails by using the Computer Vision API

Extract text from images

Extract text from images by using the OCR API
Extract text from images or PDFs by using the Read API
Convert handwritten text by using Ink Recognizer
Extract information from forms or receipts by using the prebuilt receipt model in Form

Recognizer

Build and optimize a custom model for Form Recognizer

Extract facial information from images

Detect faces in an image by using the Face API
Recognize faces in an image by using the Face API
Configure persons and person groups
Analyze facial attributes by using the Face API
Match similar faces by using the Face API

Implement image classification by using the Custom Vision service

Label images by using the Computer Vision Portal
Train a custom image classification model in the Custom Vision Portal
Train a custom image classification model by using the SDK
Manage model iterations
Evaluate classification model metrics
Publish a trained iteration of a model
Export a model in an appropriate format for a specific target
Consume a classification model from a client application
Deploy image classification custom models to containers

Implement an object detection solution by using the Custom Vision service

Label images with bounding boxes by using the Computer Vision Portal
Train a custom object detection model by using the Custom Vision Portal
Train a custom object detection model by using the SDK
Manage model iterations
Evaluate object detection model metrics
Publish a trained iteration of a model
Consume an object detection model from a client application
Deploy custom object detection models to containers

Analyze video by using Video Indexer

Process a video
Extract insights from a video
Moderate content in a video
Customize the Brands model used by Video Indexer
Customize the Language model used by Video Indexer by using the Custom Speech service
Customize the Person model used by Video Indexer
Extract insights from a live stream of video data

Analyze text by using the Text Analytics service

Retrieve and process key phrases
Retrieve and process entity information (people, places, urls, etc.)
Retrieve and process sentiment
Detect the language used in text

Manage speech by using the Speech service

Implement texttospeech
Customize texttospeech
Implement speechtotext
Improve speechtotext accuracy

Translate language

Translate text by using the Translator service
Translate speechtospeech by using the Speech service
Translate speechtotext by using the Speech service

Build an initial language model by using Language Understanding Service (LUIS)

Create intents and entities based on a schema, and then add utterances
Create complex hierarchical entities, use this instead of roles
Train and deploy a model

Iterate on and optimize a language model by using LUIS

Implement phrase lists
Implement a model as a feature (i.e. prebuilt entities)
Manage punctuation and diacritics
Implement active learning
Monitor and correct data imbalances
Implement patterns

Manage a LUIS model

Manage collaborators
Manage versioning
Publish a model through the portal or in a container
Export a LUIS package
Deploy a LUIS package to a container
Integrate Bot Framework (LUDown) to run outside of the LUIS portal

Implement a Cognitive Search solution

Create data sources
Define an index
Create and run an indexer
Query an index
Configure an index to support autocomplete and autosuggest
Boost results based on relevance
Implement synonyms

Implement an enrichment pipeline

Attach a Cognitive Services account to a skillset
Select and include builtin skills for documents
Implement custom skills and include them in a skillset

Implement a knowledge store

Define file projections
Define object projections
Define table projections
Query projections

Manage a Cognitive Search solution

Provision Cognitive Search
Configure security for Cognitive Search
Configure scalability for Cognitive Search

Manage indexing

Manage reindexing
Rebuild indexes
Schedule indexing
Monitor indexing
Implement incremental indexing
Manage concurrency
Push data to an index
Troubleshoot indexing for a pipeline

Create a knowledge base by using QnA Maker

Create a QnA Maker service
Create a knowledge base
Import a knowledge base
Train and test a knowledge base
Publish a knowledge base
Create a multiturn conversation
Add alternate phrasing
Add chitchat to a knowledge base
Export a knowledge base
Add active learning to a knowledge base
Manage collaborators

Design and implement conversation flow

Design conversation logic for a bot
Create and evaluate *.chat file conversations by using the Bot Framework Emulator
Add language generation for a response
Design and implement adaptive cards

Create a bot by using the Bot Framework SDK

Implement dialogs
Maintain state
Implement logging for a bot conversation
Implement a prompt for user input
Add and review bot telemetry
Implement a bottohuman handoff
Troubleshoot a conversational bot
Add a custom middleware for processing user messages
Manage identity and authentication
Implement channelspecific logic
Publish a bot

Create a bot by using the Bot Framework Composer

Implement dialogs
Maintain state
Implement logging for a bot conversation
Implement prompts for user input
Troubleshoot a conversational bot
Test a bot by using the Bot Framework Emulator
Publish a bot

Integrate Cognitive Services into a bot

Integrate a QnA Maker service
Integrate a LUIS service
Integrate a Speech service
Integrate Dispatch for multiple language models
Manage keys in app settings file

Preparation

At Readynez, we provide many resources and have experienced experts in the field. That is why we are also very successful with many satisfied customers. You can therefore safely take your course with us. While there are no strict entry requirements, certain foundational knowledge may enhance your learning experience. Any recommended prerequisites specific to this course are detailed below.

Proficiency in Microsoft Azure and its various services, especially those related to artificial intelligence and machine learning.
Understanding of fundamental AI concepts such as machine learning, natural language processing (NLP), computer vision, and conversational AI.
Experience in developing and deploying machine learning models using frameworks like TensorFlow, PyTorch, or Azure Machine Learning.
Knowledge of data preparation, feature engineering, model training, and evaluation techniques.
Familiarity with programming languages such as Python or R for data manipulation and model development.
Understanding of data storage and management solutions, including SQL and NoSQL databases.
Experience in deploying and managing applications in a cloud environment.
Proficiency in using development tools like Visual Studio or Azure DevOps for building and deploying AI solutions.
Strong problem-solving and analytical skills.
Completion of relevant training or certification courses on Azure fundamentals and AI concepts is recommended.

Meet our instructors

Meet some of the Readynez Instructors you can meet on your course. They are experts, passionate about what they do, and dedicated to give back to their industry, their field, and those who want to learn, explore, and advance in their careers.

Julian Sharp

Julian Sharp delivers high-quality, LIVE instructor-led training in Microsoft Dynamics 365 and Power Platform for professionals seeking real-world skills and certification readiness. With decades of industry experience and deep technical expertise, he helps learners master complex solutions efficiently.

Meet the Instructor: Julian Sharp
MCT | MVP | Solution Architect | Dynamics 365 & Power Platform Expert

With 20+ years of experience and over 16 years specializing in Dynamics CRM/365 and Power Platform, Julian has trained thousands of professionals and helped organizations solve real business challenges using Microsoft technologies.

A Microsoft Certified Trainer since 2007 and a Microsoft MVP, Julian brings a rare combination of deep technical knowledge and a practical, business-first mindset.

He’s not just an instructor - he’s a community leader, consultant, and solution architect trusted by enterprises across the globe.

Microsoft MVP – Business Applications 
Specialist in Dynamics 365, Power Platform & Azure 
Developer of Microsoft courseware
Speaker & mentor at Microsoft community events

Ready to build solutions with one of the best in the field?

Explore upcoming courses with Julian Sharp to get started.

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Tiago Costa

With a long-standing reputation as a Microsoft MVP and MCT, Tiago Costa delivers LIVE training that blends expert knowledge with real-world application. His courses help professionals build hands-on cloud skills and prepare effectively for Microsoft certifications.

Meet the Instructor: Tiago Costa

At Readynez, we believe world-class training starts with world-class instructors - and Tiago Costa is a prime example.

With over 20 years of experience delivering Microsoft Cloud & AI solutions (including to Fortune 500 companies), Tiago brings a rare combination of deep technical expertise and real-world perspective to every course.

50+ Microsoft certifications.
Microsoft MVP since 2016 & Microsoft Certified Trainer since 2006. 
Speaker, architect, and trusted advisor to global enterprises.

As a dedicated community leader, an independent contractor and founder of the Azure & AI Portugal User Group, Tiago is passionate about helping others grow their careers through knowledge sharing and practical skills.

When you train with Tiago, you’re not just learning from certified professionals...
You’re learning from a global leader.

Find your next Microsoft course with Tiago Costa here.

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FAQ

FAQs for the Microsoft Certified Azure AI Engineer (AI-102) course.

What is Microsoft Certified Azure AI Engineer certification?

Microsoft Certified Azure AI Engineer certification validates expertise in designing and implementing AI solutions on Azure. It demonstrates proficiency in leveraging Azure AI services to build intelligent applications and solve real-world business problems using artificial intelligence technologies.

How do I prepare for the AI-102 exam?

Join our Microsoft Certified Azure AI Engineer course (AI-102) and unlock the potential to design and implement AI solutions on Azure. At Readynez, we provide the training and support you need to pass the exam and become certified in this essential field. Take your career to new heights in artificial intelligence with our expert guidance and hands-on learning approach.

What are the prerequisites for Microsoft Certified Azure AI Engineer certification?

Prerequisites for Microsoft Certified Azure AI Engineer certification include familiarity with Azure services, machine learning, and programming languages like Python. Experience in designing and implementing AI solutions, along with knowledge of Azure cloud infrastructure, is recommended for success in this certification.

How much does the AI-102 exam cost?

The AI-102 exam is priced at approximately €140. This exam consists of 40-60 multiple-choice questions, and candidates are allotted a maximum of 130 minutes to complete it.

What topics are covered in the AI-102 exam?

The AI-102 exam covers a wide range of topics related to Azure AI services, including Azure Cognitive Services, natural language processing, computer vision, and machine learning model deployment on Azure. It assesses candidates' abilities to design and implement AI solutions using various Azure technologies.

Is Microsoft Certified Azure AI Engineer certification worth it for my career?

Yes, Microsoft Certified Azure AI Engineer certification is highly valuable for professionals seeking to advance their careers in AI development, data science, and cloud computing. It demonstrates expertise in designing and implementing AI solutions on Azure, opening up new opportunities for career growth and advancement.

How How long does it take to become Microsoft Certified Azure AI Engineer certified?

The time it takes to become Microsoft Certified Azure AI Engineer certified varies depending on individual experience, study habits, and dedication. Typically, candidates spend a few months preparing for the AI-102 exam by studying relevant materials, practising with hands-on labs, and taking practice tests.

Can I take the AI-102 exam online?

Yes, the AI-102 exam can be taken online from the comfort of your own home or office. Microsoft offers online proctoring options for many of its certification exams, including the AI-102 exam. Ensure your computer meets the technical requirements and follow the registration process to schedule your online exam session.

How easy or difficult is it to pass the AI-102 exam?

The difficulty of passing the AI-102 exam varies based on individual experience, preparation, and familiarity with the exam topics. Candidates with a solid understanding of Azure AI services and machine learning concepts, combined with effective study strategies, are more likely to succeed in passing the exam.

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

The passing score for the AI-102 exam is 700 or greater (out of 1000).

How can I maintain my Microsoft Certified Azure AI Engineer certification?

Maintain your Microsoft Certified Azure AI Engineer certification by staying updated with the latest Azure AI technologies and completing recertification requirements as outlined by Microsoft. Participate in relevant training programs, attend conferences, and engage in continuous learning to keep your skills sharp and up-to-date.

How much salary can I get after getting Microsoft Certified Azure AI Engineer certified?

Salary potential after obtaining Microsoft Certified Azure AI Engineer certification varies depending on factors such as location, industry, experience, and job role. Generally, professionals with Azure AI expertise command competitive salaries in fields such as AI development, data science, and cloud computing, offering excellent earning potential.

Reviews

Feedback from our Microsoft Certified Azure AI Engineer delegates.

Stephen Ridgway

Readynez is the best training provider I've used for many years. Their customer service is first class, prices are very competitive and instruction excellent.

Johan Andersson

Easy to attend over Teams and an excellent instructor gave me great value for the time I invested.

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