Study guide for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution
Note
Cognitive Services has been renamed to Azure AI Services. Individual services have also been renamed. These changes will appear on the exam in late 2023. Learn more about Azure AI services.
Purpose of this document
This study guide should help you understand what to expect on the exam and includes a summary of the topics the exam might cover and links to additional resources. The information and materials in this document should help you focus your studies as you prepare for the exam.
| Useful links | Description |
|---|---|
| Review the skills measured as of August 23, 2023 | This list represents the skills measured AFTER the date provided. Study this list if you plan to take the exam AFTER that date. |
| Review the skills measured prior to August 23, 2023 | Study this list of skills if you take your exam PRIOR to the date provided. |
| Change log | You can go directly to the change log if you want to see the changes that will be made on the date provided. |
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Updates to the exam
Our exams are updated periodically to reflect skills that are required to perform a role. We have included two versions of the Skills Measured objectives depending on when you are taking the exam.
We always update the English language version of the exam first. Some exams are localized into other languages, and those are updated approximately eight weeks after the English version is updated. While Microsoft makes every effort to update localized exams as noted, there may be times when the localized versions of an exam are not updated on this schedule. Other available languages are listed in the Schedule Exam section of the Exam Details webpage. If the exam isn't available in your preferred language, you can request an additional 30 minutes to complete the exam.
Note
The bullets that follow each of the skills measured are intended to illustrate how we are assessing that skill. Related topics may be covered in the exam.
Note
Most questions cover features that are general availability (GA). The exam may contain questions on Preview features if those features are commonly used.
Skills measured as of August 23, 2023
Audience profile
Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of Azure Cognitive Services and Azure Applied AI services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.
These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.
Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.
Plan and manage an Azure AI solution (25–30%)
Implement image and video processing solutions (15–20%)
Implement natural language processing solutions (25–30%)
Implement knowledge mining solutions (5–10%)
Implement conversational AI solutions (15–20%)
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 content
Create a solution that uses 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
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 image classification and object detection
Manage training iterations
Evaluate model metrics
Publish a trained 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
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 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 an orchestration workflow
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
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
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
Study resources
We recommend that you train and get hands-on experience before you take the exam. We offer self-study options and classroom training as well as links to documentation, community sites, and videos.
| Study resources | Links to learning and documentation |
|---|---|
| Get trained | Choose from self-paced learning paths and modules or take an instructor-led course |
| Find documentation | Azure Cognitive Services Computer Vision Azure Video Indexer Language Understanding Speech to Text Speech Translation Azure Cognitive Search Azure Bot Service |
| Ask a question | Microsoft Q&A | Microsoft Docs |
| Get community support | AI - Machine Learning - Microsoft Tech Community AI - Machine Learning Blog - Microsoft Tech Community |
| Follow Microsoft Learn | Microsoft Learn - Microsoft Tech Community |
| Find a video | The AI Show Browse other Microsoft Learn shows |
Change log
Key to understanding the table: The topic groups (also known as functional groups) are in bold typeface followed by the objectives within each group. The table is a comparison between the two versions of the exam skills measured and the third column describes the extent of the changes.
| Skill area prior to August 23, 2023 | Skill area as of August 23, 2023 | Changes |
|---|---|---|
| Audience profile | Minor | |
| Plan and manage an Azure AI solution | Plan and manage an Azure AI solution | No change |
| Select the appropriate Azure AI service | Select the appropriate Azure AI service | No change |
| Plan and configure security for Azure AI services | Plan and configure security for Azure AI services | No change |
| Create and manage an Azure AI service | Create and manage an Azure AI service | No change |
| Deploy Azure AI services | Deploy Azure AI services | No change |
| Create solutions to detect anomalies and improve content | Create solutions to detect anomalies and improve content | No change |
| Implement image and video processing solutions | Implement image and video processing solutions | No change |
| Analyze images | Analyze images | No change |
| Extract text from images | Extract text from images | No change |
| Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services | Implement image classification and object detection by using the Custom Vision service, part of Azure Cognitive Services | Minor |
| Process videos | Process videos | No change |
| Implement natural language processing solutions | Implement natural language processing solutions | No change |
| Analyze text | Analyze text | No change |
| Process speech | Process speech | No change |
| Translate language | Translate language | No change |
| Build and manage a language understanding model | Build and manage a language understanding model | Minor |
| Create a question answering solution | Create a question answering solution | No change |
| Implement knowledge mining solutions | Implement knowledge mining solutions | No change |
| Implement a Cognitive Search solution | Implement a Cognitive Search solution | No change |
| Apply AI enrichment skills to an indexer pipeline | Apply AI enrichment skills to an indexer pipeline | No change |
| Implement conversational AI solutions | Implement conversational AI solutions | No change |
| Design and implement conversation flow | Design and implement conversation flow | No change |
| Build a conversational bot | Build a conversational bot | No change |
| Test, publish, and maintain a conversational bot | Test, publish, and maintain a conversational bot | No change |
Skills measured prior to August 23, 2023
Audience profile
Microsoft Azure AI engineers build, manage, and deploy AI solutions that make the most of Azure Cognitive Services and Azure services. Their responsibilities include participating in all phases of AI solutions development—from requirements definition and design to development, deployment, integration, maintenance, performance tuning, and monitoring.
These professionals work with solution architects to translate their vision and with data scientists, data engineers, IoT specialists, infrastructure administrators, and other software developers to build complete end-to-end AI solutions.
Azure AI engineers have experience developing solutions that use languages such as Python or C# and should be able to use REST-based APIs and software development kits (SDKs) to build secure image processing, video processing, natural language processing (NLP), knowledge mining, and conversational AI solutions on Azure. They should be familiar with all methods of implementing AI solutions. Plus, they understand the components that make up the Azure AI portfolio and the available data storage options. Azure AI engineers also need to understand and be able to apply responsible AI principles.
Plan and manage an Azure AI solution (25–30%)
Implement image and video processing solutions (15–20%)
Implement natural language processing solutions (25–30%)
Implement knowledge mining solutions (5–10%)
Implement conversational AI solutions (15–20%)
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 content
Create a solution that uses 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
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
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 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
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
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