Free Practice Questions for Microsoft Azure AI Engineer Associate (AI-102) Certification

- Select the appropriate service for a generative AI solution - 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 speech solution - Select the appropriate service for an information extraction solution - Select the appropriate service for a knowledge mining solution

- Plan for a solution that meets Responsible AI principles - Create an Azure AI resource - Choose the appropriate AI models for your solution - Deploy AI models using the appropriate deployment options - Install and utilize the appropriate SDKs and APIs - Determine a default endpoint for a service - Integrate Microsoft Foundry Services into a continuous integration and continuous delivery (CI/CD) pipeline - Plan and implement a container deployment

- Monitor an Azure AI resource - Manage costs for Microsoft Foundry Services - Manage and protect account keys - Manage authentication for a Microsoft Foundry Service resource

- Implement content moderation solutions - Configure responsible AI insights, including content safety - Implement responsible AI, including content filters and blocklists - Prevent harmful behavior, including prompt shields and harm detection - Design a responsible AI governance framework

- Plan and prepare for a generative AI solution - Deploy a hub, project, and necessary resources with Microsoft Foundry - Deploy the appropriate generative AI model for your use case - Implement a prompt flow solution - Implement a RAG pattern by grounding a model in your data - Evaluate models and flows - Integrate your project into an application with Microsoft Foundry SDK - Utilize prompt templates in your generative AI solution

- Provision an Azure OpenAI in Foundry Models resource - Select and deploy an Azure OpenAI model - Submit prompts to generate code and natural language responses - Use the DALL-E model to generate images - Integrate Azure OpenAI into your own application - Use large multimodal models in Azure OpenAI

- Configure parameters to control generative behavior - Configure model monitoring and diagnostic settings, including performance and resource consumption - Optimize and manage resources for deployment, including scalability and foundational model updates - Enable tracing and collect feedback - Implement model reflection - Deploy containers for use on local and edge devices - Implement orchestration of multiple generative AI models - Apply prompt engineering techniques to improve responses - Fine-tune a generative model

- Understand the role and use cases of an agent - Configure the necessary resources to build an agent - Create an agent with the Microsoft Foundry Agent Service - Implement complex agents with Microsoft Agent Framework - Implement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilities - Test, optimize and deploy an agent

- Select visual features to meet image processing requirements - Detect objects in images and generate image tags - Include image analysis features in an image processing request - Interpret image processing responses - Extract text from images using Azure Vision in Foundry Tools - Convert handwritten text using Azure Vision in Foundry Tools

- Choose between image classification and object detection models - Label images - Train a custom image model, including image classification and object detection - Evaluate custom vision model metrics - Publish a custom vision model - Consume a custom vision model - Build a custom vision model code first

- Use Azure AI Video Indexer to extract insights from a video or live stream - Use Azure Vision in Foundry Tools Spatial Analysis to detect presence and movement of people in video

- Extract key phrases and entities - Determine sentiment of text - Detect the language used in text - Detect personally identifiable information (PII) in text - Translate text and documents by using the Azure Translator in Foundry Tools service

- Integrate generative AI speaking capabilities in an application - Implement text-to-speech and speech-to-text using Azure Speech in Foundry Tools - Improve text-to-speech by using Speech Synthesis Markup Language (SSML) - Implement custom speech solutions with Azure Speech in Foundry Tools - Implement intent and keyword recognition with Azure Speech in Foundry Tools - Translate speech-to-speech and speech-to-text by using the Azure Speech in Foundry Tools service

- Create intents, entities, and add utterances - Train, evaluate, deploy, and test a language understanding model - Optimize, backup, and recover language understanding model - Consume a language model from a client application - Create a custom question answering project - Add question-and-answer pairs and import sources for question answering - Train, test, and publish a knowledge base - Create a multi-turn conversation - Add alternate phrasing and chit-chat to a knowledge base - Export a knowledge base - Create a multi-language question answering solution - Implement custom translation, including training, improving, and publishing a custom model

- Provision an Azure AI Search resource, create an index, and define a skillset - Create data sources and indexers - Implement custom skills and include them in a skillset - Create and run an indexer - Query an index, including syntax, sorting, filtering, and wildcards - Manage Knowledge Store projections, including file, object, and table projections - Implement semantic and vector store solutions

- Provision a Document Intelligence resource - Use prebuilt models to extract data from documents - Implement a custom document intelligence model - Train, test, and publish a custom document intelligence model - Create a composed document intelligence model

- Create an OCR pipeline to extract text from images and documents - Summarize, classify, and detect attributes of documents - Extract entities, tables, and images from documents - Process and ingest documents, images, videos, and audio with Azure Content Understanding in Foundry Tools