AI-102T00 - Designing and Implementing a Microsoft Azure AI Solution

AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The Certification & Training course will use C# or Python as the programming language.

Code: ai-102t00

Duration: 4.0 days

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  • Define artificial intelligence
  • Understand AI-related terms
  • Understand considerations for AI Engineers
  • Understand considerations for responsible AI
  • Understand capabilities of Azure Machine Learning
  • Understand capabilities of Azure AI Services
  • Understand capabilities of Azure OpenAI Service
  • Understand capabilities of Azure AI Search
  • Create Azure AI services resources in an Azure subscription.
  • Identify endpoints, keys, and locations required to consume an Azure AI services resource.
  • Use a REST API and an SDK to consume Azure AI services.
  • Consider authentication for Azure AI services
  • Manage network security for Azure AI services
  • Monitor Azure AI services costs.
  • Create alerts and view metrics for Azure AI services.
  • Manage Azure AI services diagnostic logging.
  • Create containers for reuse
  • Deploy to a container and secure a container
  • Consume Azure AI services from a container
  • Provision an Azure AI Vision resource
  • Analyze an image
  • Generate a smart-cropped thumbnail
  • Provision Azure resources for Azure AI Custom Vision
  • Understand image classification
  • Train an image classifier
  • Identify options for face detection, analysis, and identification.
  • Understand considerations for face analysis.
  • Detect faces with the Computer Vision service.
  • Understand capabilities of the Face service.
  • Compare and match detected faces.
  • Implement facial recognition.
  • Read text from images using OCR
  • Use the Azure AI Vision service Image Analysis with SDKs and the REST API
  • Develop an application that can read printed and handwritten text
  • Describe Azure Video Indexer capabilities
  • Extract custom insights
  • Use Azure Video Indexer widgets and APIs
  • Detect language from text
  • Analyze text sentiment
  • Extract key phrases, entities, and linked entities
  • Understand question answering and how it compares to language understanding
  • Create, test, publish and consume a knowledge base
  • Implement multi-turn conversation and active learning
  • Create a question answering bot to interact with using natural language
  • Provision Azure resources for Azure AI Language resource
  • Define intents, utterances, and entities
  • Use patterns to differentiate similar utterances
  • Use pre-built entity components
  • Train, test, publish, and review an Azure AI Language model
  • Understand types of classification projects
  • Build a custom text classification project
  • Tag data, train, and deploy a model
  • Submit classification tasks from your own app
  • Understand tagging entities in extraction projects
  • Understand how to build entity recognition projects
  • Provision a Translator resource
  • Understand language detection, translation, and transliteration
  • Specify translation options
  • Define custom translations
  • Provision an Azure resource for the Azure AI Speech service
  • Use the Azure AI Speech to text API to implement speech recognition
  • Use the Text to speech API to implement speech synthesis
  • Configure audio format and voices
  • Use Speech Synthesis Markup Language (SSML)
  • Provision Azure resources for speech translation.
  • Generate text translation from speech.
  • Synthesize spoken translations.
  • Create an Azure AI Search solution
  • Develop a search application
  • Implement a custom skill for Azure AI Search
  • Integrate a custom skill into an Azure AI Search skillset
  • Create a knowledge store from an Azure AI Search pipeline
  • View data in projections in a knowledge store
  • Describe the components of an Azure AI Document Intelligence solution.
  • Create and connect to Azure AI Document Intelligence resources in Azure.
  • Choose whether to use a prebuilt, custom, or composed model.
  • Identify business problems that you can solve by using prebuilt models in Forms Analyzer.
  • Analyze forms by using the General Document, Read, and Layout models.
  • Analyze forms by using financial, ID, and tax prebuilt models
  • Identify how Azure Document Intelligence's layout service, prebuilt models, and custom service can automate processes
  • Use Azure Document Intelligence's Optical Character Recognition (OCR) capabilities with SDKs, REST API, and Azure Document Intelligence Studio
  • Develop and test custom models
  • Create an Azure OpenAI Service resource and understand types of Azure OpenAI base models.
  • Use the Azure OpenAI Studio, console, or REST API to deploy a base model and test it in the Studio's playgrounds.
  • Generate completions to prompts and begin to manage model parameters.
  • Integrate Azure OpenAI into your application
  • Differentiate between different endpoints available to your application
  • Generate completions to prompts using the REST API and language specific SDKs
  • Understand the concept of prompt engineering and its role in optimizing Azure OpenAI models' performance.
  • Know how to design and optimize prompts to better utilize AI models.
  • Include clear instructions, request output composition, and use contextual content to improve the quality of the model's responses.
  • Use natural language prompts to write code
  • Build unit tests and understand complex code with AI models
  • Generate comments and documentation for existing code
  • Describe the capabilities of DALL-E in the Azure openAI service
  • Use the DALL-E playground in Azure OpenAI Studio
  • Use the Azure OpenAI REST interface to integrate DALL-E image generation into your apps
  • Describe the capabilities of Azure OpenAI on your data
  • Configure Azure OpenAI to use your own data
  • Use Azure OpenAI API to generate responses based on your own data
  • Describe an overall process for responsible generative AI solution development
  • Identify and prioritize potential harms relevant to a generative AI solution
  • Measure the presence of harms in a generative AI solution
  • Mitigate harms in a generative AI solution
  • Prepare to deploy and operate a generative AI solution responsibly


1. Prepare to develop AI solutions on Azure

As an aspiring Azure AI Engineer, you should understand core concepts and principles of AI development, and the capabilities of Azure services used in AI solutions.

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2. Create and consume Azure AI services

Azure AI services enable developers to easily add AI capabilities into their applications. Learn how to create and consume these services.

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3. Secure Azure AI services

Securing Azure AI services can help prevent data loss and privacy violations for user data that may be a part of the solution.

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4. Monitor Azure AI services

Azure AI services enable you to integrate artificial intelligence into your applications and services. It's important to be able to monitor Azure AI Services in order to track utilization, determine trends, and detect and troubleshoot issues.

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5. Deploy Azure AI services in containers

Learn about Container support in Azure AI services allowing the use of APIs available in Azure and enable flexibility in where to deploy and host the services with Docker containers.

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6. Analyze images

With the Azure AI Vision service, you can use pre-trained models to analyze images and extract insights and information from them.

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7. Classify images

Image classification is used to determine the main subject of an image. You can use the Azure AI Custom Vision services to train a model that classifies images based on your own categorizations.

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8. Detect, analyze, and recognize faces

The ability for applications to detect human faces, analyze facial features and emotions, and identify individuals is a key artificial intelligence capability.

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9. Read Text in images and documents with the Azure AI Vision Service

Azure's AI Vision service uses algorithms to process images and return information. This module teaches you how to use the Image Analysis API for optical character recognition (OCR).

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10. Analyze video

Azure Video Indexer is a service to extract insights from video, including face identification, text recognition, object labels, scene segmentations, and more.

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11. Analyze text with Azure AI Language

The Azure AI Language service enables you to create intelligent apps and services that extract semantic information from text.

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12. Build a question answering solution

The question answering capability of the Azure AI Language service makes it easy to build applications in which users ask questions using natural language and receive appropriate answers.

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13. Build a conversational language understanding model

The Azure AI Language conversational language understanding service (CLU) enables you to train a model that apps can use to extract meaning from natural language.

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14. Create a custom text classification solution

The Azure AI Language service enables processing of natural language to use in your own app. Learn how to build a custom text classification project.

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15. Custom named entity recognition

Build a custom entity recognition solution to extract entities from unstructured documents

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16. Translate text with Azure AI Translator service

The Translator service enables you to create intelligent apps and services that can translate text between languages.

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17. Create speech-enabled apps with Azure AI services

The Azure AI Speech service enables you to build speech-enabled applications. This module focuses on using the speech-to-text and text to speech APIs, which enable you to create apps that are capable of speech recognition and speech synthesis.

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18. Translate speech with the Azure AI Speech service

Translation of speech builds on speech recognition by recognizing and transcribing spoken input in a specified language, and returning translations of the transcription in one or more other languages.

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19. Create an Azure AI Search solution

Unlock the hidden insights in your data with Azure AI Search.

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20. Create a custom skill for Azure AI Search

Use the power of artificial intelligence to enrich your data and find new insights.

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21. Create a knowledge store with Azure AI Search

Persist the output from an Azure AI Search enrichment pipeline for independent analysis or downstream processing.

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22. Plan an Azure AI Document Intelligence solution

Learn how to use Azure AI Document Intelligence to build solutions that analyze forms and output data for storage or further processing.

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23. Use prebuilt Form Recognizer models

Learn what data you can analyze by choosing prebuilt Forms Analyzer models and how to deploy these models in a Form Analyzer solution.

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24. Extract data from forms with Azure Document Intelligence

Azure Document Intelligence uses machine learning technology to identify and extract key-value pairs and table data from form documents with accuracy, at scale. This module teaches you how to use the Azure Document Intelligence Azure AI service.

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25. Get started with Azure OpenAI Service

This module provides engineers with the skills to begin building an Azure OpenAI Service solution.

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26. Build natural language solutions with Azure OpenAI Service

This module provides engineers with the skills to begin building apps that integrate with the Azure OpenAI Service.

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27. Apply prompt engineering with Azure OpenAI Service

Prompt engineering in Azure OpenAI is a technique that involves designing prompts for natural language processing models. This process improves accuracy and relevancy in responses, optimizing the performance of the model.

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28. Generate code with Azure OpenAI Service

This module shows engineers how to use the Azure OpenAI Service to generate and improve code.

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29. Generate images with Azure OpenAI Service

The Azure OpenAI service includes the DALL-E model, which you can use to generate original images based on natural language prompts.

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30. Implement Retrieval Augmented Generation (RAG) with Azure OpenAI Service

Azure OpenAI on your data allows developers to implement RAG with supported AI chat models to reference specific sources of data to ground the response.

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31. Fundamentals of Responsible Generative AI

Generative AI enables amazing creative solutions, but must be implemented responsibly to minimize the risk of harmful content generation.

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Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.


Before attending this course, students must have:

  • Knowledge of Microsoft Azure and ability to navigate the Azure portal
  • Knowledge of either C# or Python
  • Familiarity with JSON and REST programming semantics

To gain C# or Python skills, complete the free Take your first steps with C# or Take your first steps with Python learning path before attending the course.

If you are new to artificial intelligence, and want an overview of AI capabilities on Azure, consider completing the Azure AI Fundamentals certification before taking this one.



Skills Measured

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

Course Benefits

  • Career growth
  • Broad Career opportunities
  • Worldwide recognition from leaders
  • Up-to Date technical skills
  • Popular Certification Badges

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