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

  • Exam Code: AI-102
  • Exam Name: Designing and Implementing a Microsoft Azure AI Solution
  • Updated: Jul 04, 2026
  • Q & A: 425 Questions and Answers

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About Microsoft AI-102 Exam Questions

Microsoft AI-102 Exam Syllabus Topics:

TopicDetails

Plan and Manage an Azure Cognitive Services Solution (15-20%)

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, Languages, Speech, Form Recognizer)
- deploy Cognitive Services Containers in Microsoft Azure

Implement Computer Vision Solutions (20-25%)

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 or PDFs by using the Computer Vision service
- extract information using pre-built models 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
- 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 Azure Video Analyzer for Media (formerly 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

Implement Natural Language Processing Solutions (20-25%)

Analyze text by using the Language 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 text-to-speech
- customize text-to-speech
- implement speech-to-text
- improve speech-to-text accuracy
- improve text-to-speech accuracy
- implement intent recognition
Translate language- translate text by using the Translator service
- translate speech-to-speech by using the Speech service
- translate speech-to-text by using the Speech service
Build a initial language model by using Language Understanding Service (LUIS)- create intents and entities based on a schema, and 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 Language Understanding- 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 Language Understanding 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
Create a Questions Answering solution using the Language service- create a question answering project
- import questions and answers
- 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
- add active learning to a knowledge base

Implement Knowledge Mining Solutions (15-20%)

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 built-in 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 re-indexing
- rebuild indexes
- schedule indexing
- monitor indexing
- implement incremental indexing
- manage concurrency
- push data to an index
- troubleshoot indexing for a pipeline

Implement Conversational AI Solutions (15-20%)

Design and implement conversation flow- design conversation logic for a bot
- create and evaluate *.chat file conversations by using the Bot Framework Emulator
- choose an appropriate conversational model for a bot, including activity handlers and dialogs
Create a bot by using the Bot Framework SDK- use the Bot Framework SDK to create a bot from a template
- implement activity handlers and dialogs
- use Turn Context
- test a bot using the Bot Framework Emulator
- deploy a bot to Azure
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
- publish a bot
- add language generation for a response
- design and implement adaptive cards
Integrate Cognitive Services into a bot- integrate a question answering model
- integrate a LUIS service
- integrate a Speech service resource

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How to book the AI-102: Designing and Implementing an Azure AI Solution Exam

These are following steps for registering the AI-102: Designing and Implementing an Azure AI Solution exam.

  • Step 1: Visit to Microsoft Learning and search for AI-102: Developing Solutions for Microsoft Azure.
  • Step 2: Sign up/Login to Pearson VUE account
  • Step 3: Select local centre based on your country, date, time and confirm with a payment method.

Reference: https://docs.microsoft.com/en-us/learn/certifications/exams/ai-102

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