Designing and Implementing a Microsoft Azure AI Solution (AI-102) Practice Exam
Designing and Implementing a Microsoft Azure AI Solution (AI-102)
About Designing and Implementing a Microsoft Azure AI Solution (AI-102) Practice Exam
Designing and Implementing a Microsoft Azure AI Solution AI-102 certification exam measures your ability to accomplish technical tasks including - plan and manage Azure Cognitive Services solutions; implement Computer Vision solutions; implement natural language processing solutions; implement knowledge mining solutions, and implement conversational AI solutions.
Who should take the AI-102 exam?
- Candidates for Exam AI-102 should have subject matter expertise 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#, Python, or JavaScript 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.
- Candidates 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.
Microsoft Azure AI Solution AI-102 Course Outline
The Designing and Implementing a Microsoft Azure AI Solution (AI-102) Exam covers the latest exam updates and topics -
Module 1 - Describe Plan and manage an Azure AI solution (15–20%)
1.1 Explain selecting the appropriate Azure AI service
- Select the appropriate service for a computer vision solution
- Select the appropriate service for a natural language processing solution
- Select the appropriate service for a decision support solution
- Select the appropriate service for a speech solution
- Select the appropriate service for a generative AI solution
- Select the appropriate service for a document intelligence solution
- Select the appropriate service for a knowledge mining solution
1.2 Explain Planning, creating and deploying an Azure AI service
- Learn to plan for a solution that meets Responsible AI principles
- Learn to create an Azure AI resource
- Learn to determine a default endpoint for a service
- Learn to integrate Azure AI services into a continuous integration and continuous delivery (CI/CD) pipeline
- Learn to plan and implement a container deployment
1.3 Explain managing, monitoring and securing an Azure AI service
- Learn to configure diagnostic logging
- Learn to monitor an Azure AI resource
- Learn to manage costs for Azure AI services
- Learn to manage account keys
- Learn to protect account keys by using Azure Key Vault
- Learn to manage authentication for an Azure AI Service resource
- Learn to manage private communications
Domain 2 - Understand to Implement content moderation solutions (10–15%)
Learn to Create solutions for content delivery
- Explain to implement a text moderation solution with Azure AI Content Safety
- Explain to implement an image moderation solution with Azure AI Content Safety
Module 3 - Implement computer vision solutions (15–20%)
3.1 Explain analyzing images
- Learn to select visual features to meet image processing requirements
- Learn to detect objects in images and generate image tags
- Learn to include image analysis features in an image processing request
- Learn to interpret image processing responses
- Learn to extract text from images using Azure AI Vision
- Learn to convert handwritten text using Azure AI Vision
3.3 Explain implementing custom computer vision models by using Azure AI Vision
- Learn to choose between image classification and object detection models
- Learn about Label images
- Learn to train a custom image model, including image classification and object detection
- Learn to evaluate custom vision model metrics
- Learn to publish a custom vision model
- Learn to consume a custom vision model
3.4 Explain analyzing videos
- Learn to use Azure AI Video Indexer to extract insights from a video or live stream
- Learn to use Azure AI Vision Spatial Analysis to detect presence and movement of people in video
Module 4 - Describe implementing Natural Language Processing (NLP) solutions (30–35%)
4.1 Explain to analyze text by using Azure AI Language
- Learn to extract key phrases
- Learn to extract entities
- Learn to determine sentiment of text
- Learn to detect the language used in text
- Learn to detect personally identifiable information (PII) in text
4.2 Explain Process speech by using Azure AI Speech
- Learn to Implement text-to-speech
- Implement speech-to-text
- Improve text-to-speech by using Speech Synthesis Markup Language (SSML)
- Implement custom speech solutions
- Implement intent recognition
- Implement keyword recognition
4.3 Explain to translate language
- Learn to translate text and documents by using the Azure AI Translator service
- Learn to implement custom translation, including training, improving, and publishing a custom model
- Learn to translate speech-to-speech by using the Azure AI Speech service
- Learn to translate speech-to-text by using the Azure AI Speech service
- Learn to translate to multiple languages simultaneously
4.4 Explain to implementing and managing a language understanding model by using Azure AI Language
- Learn to create intents and add utterances
- Learn to create entities
- Learn to train, evaluate, deploy, and test a language understanding model
- Learn to optimize a language understanding model
- Learn to consume a language model from a client application
- Learn to backup and recover language understanding models
4.5 Explain creating a question answering solution by using Azure AI Language
- Learn to create a question answering project
- Learn to add question-and-answer pairs manually
- Learn to import sources
- Learn to train and test a knowledge base
- Learn to publish a knowledge base
- Learn to create a multi-turn conversation
- Learn to add alternate phrasing
- Learn to add chit-chat to a knowledge base
- Learn to export a knowledge base
- Learn to create a multi-language question answering solution
Domain 5 - Describe implementing knowledge mining and document intelligence solutions (10–15%)
5.1 Explain implementing an Azure Cognitive Search solution
- Learn to provision a Cognitive Search resource
- Learn to create data sources
- Learn to create an index
- Learn to define a skillset
- Learn to implement custom skills and include them in a skillset
- Learn to create and run an indexer
- Learn to query an index, including syntax, sorting, filtering, and wildcards
- Learn to manage Knowledge Store projections, including file, object, and table projections
5.2 Explain implementing an Azure AI Document Intelligence solution
- Learn to Provision a Document Intelligence resource
- Learn to use prebuilt models to extract data from documents
- Learn to implement a custom document intelligence model
- Learn to train, test, and publish a custom document intelligence model
- Learn to create a composed document intelligence model
- Learn to implement a document intelligence model as a custom Azure Cognitive Search skill
Domain 6 - Describe implementing generative AI solutions (10–15%)
6.1 Explain using Azure OpenAI Service to generate content
- Learn to provision an Azure OpenAI Service resource
- Learn to select and deploy an Azure OpenAI model
- Learn to submit prompts to generate natural language
- Learn to submit prompts to generate code
- Learn to use the DALL-E model to generate images
- Learn to use Azure OpenAI APIs to submit prompts and receive responses
6.2 Explain Optimize generative AI
- Learn to configure parameters to control generative behavior
- Learn to apply prompt engineering techniques to improve responses
- Learn to use your own data with an Azure OpenAI model
- Learn to fine-tune an Azure OpenAI model
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- Practice exam questions have been created on the basis of content outlined in the official documentation.
- Each set in the practice exam contains unique questions built with the intent to provide real-time experience to the candidates as well as gain more confidence during exam preparation.
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