Practice Exams | Microsoft Azure AI-102 | Azure AI Solution

In order to set realistic expectations, please note: These questions are NOT official questions that you will find on the official exam. These questions DO cover all the material outlined in the knowledge sections below. Many of the questions are based on fictitious scenarios which have questions posed within them.The official knowledge requirements for the exam are reviewed routinely to ensure that the content has the latest requirements incorporated in the practice questions. Updates to content are often made without prior notification and are subject to change at any time.Each question has a detailed explanation and links to reference materials to support the answers which ensures accuracy of the problem solutions.The questions will be shuffled each time you repeat the tests so you will need to know why an answer is correct, not just that the correct answer was item "B"  last time you went through the test.NOTE: This course should not be your only study material to prepare for the official exam. These practice tests are meant to supplement topic study material.As a Microsoft Azure AI engineer, you build, manage, and deploy AI solutions that leverage Azure AI.Your responsibilities include participating in all phases of AI solutions development, including:Requirements definition and designDevelopmentDeploymentIntegrationMaintenancePerformance tuningMonitoringYou work with solution architects to translate their vision. You also work with data scientists, data engineers, Internet of Things (IoT) specialists, infrastructure administrators, and other software developers to:Build complete and secure end-to-end AI solutions.Integrate AI capabilities in other applications and solutions.As an Azure AI engineer, you have experience developing solutions that use languages such as:PythonC#You should be able to use Representational State Transfer (REST) APIs and SDKs to build secure image processing, video processing, natural language processing, knowledge mining, and generative AI solutions on Azure. You should:Understand the components that make up the Azure AI portfolio and the available data storage options.Be able to apply responsible AI principles.Skills at a glancePlan and manage an Azure AI solution (20–25%)Implement generative AI solutions (15–20%)Implement an agentic solution (5–10%)Implement computer vision solutions (10–15%)Implement natural language processing solutions (15–20%)Implement knowledge mining and information extraction solutions (15–20%)Plan and manage an Azure AI solution (20–25%)Select the appropriate Azure AI Foundry servicesSelect the appropriate service for a generative AI solutionSelect the appropriate service for a computer vision solutionSelect the appropriate service for a natural language processing solutionSelect the appropriate service for a speech solutionSelect the appropriate service for an information extraction solutionSelect the appropriate service for a knowledge mining solutionPlan, create and deploy an Azure AI Foundry servicePlan for a solution that meets Responsible AI principlesCreate an Azure AI resourceChoose the appropriate AI models for your solutionDeploy AI models using the appropriate deployment optionsInstall and utilize the appropriate SDKs and APIsDetermine a default endpoint for a serviceIntegrate Azure AI Foundry Services into a continuous integration and continuous delivery (CI/CD) pipelinePlan and implement a container deploymentManage, monitor, and secure an Azure AI Foundry ServiceMonitor an Azure AI resourceManage costs for Azure AI Foundry ServicesManage and protect account keysManage authentication for an Azure AI Foundry Service resourceImplement AI solutions responsiblyImplement content moderation solutionsConfigure responsible AI insights, including content safetyImplement responsible AI, including content filters and blocklistsPrevent harmful behavior, including prompt shields and harm detectionDesign a responsible AI governance frameworkImplement generative AI solutions (15–20%)Build generative AI solutions with Azure AI FoundryPlan and prepare for a generative AI solutionDeploy a hub, project, and necessary resources with Azure AI FoundryDeploy the appropriate generative AI model for your use caseImplement a prompt flow solutionImplement a RAG pattern by grounding a model in your dataEvaluate models and flowsIntegrate your project into an application with Azure AI Foundry SDKUtilize prompt templates in your generative AI solutionUse Azure OpenAI in Foundry Models to generate contentProvision an Azure OpenAI in Foundry Models resourceSelect and deploy an Azure OpenAI modelSubmit prompts to generate code and natural language responsesUse the DALL-E model to generate imagesIntegrate Azure OpenAI into your own applicationUse large multimodal models in Azure OpenAIImplement an Azure OpenAI AssistantOptimize and operationalize a generative AI solutionConfigure parameters to control generative behaviorConfigure model monitoring and diagnostic settings, including performance and resource consumptionOptimize and manage resources for deployment, including scalability and foundational model updatesEnable tracing and collect feedbackImplement model reflectionDeploy containers for use on local and edge devicesImplement orchestration of multiple generative AI modelsApply prompt engineering techniques to improve responsesFine-tune an generative modelImplement an agentic solution (5–10%)Create custom agentsUnderstand the role and use cases of an agentConfigure the necessary resources to build an agentCreate an agent with the Azure AI Foundry Agent ServiceImplement complex agents with Semantic Kernel and AutogenImplement complex workflows including orchestration for a multi-agent solution, multiple users, and autonomous capabilitiesTest, optimize and deploy an agentImplement computer vision solutions (10–15%)Analyze imagesSelect visual features to meet image processing requirementsDetect objects in images and generate image tagsInclude image analysis features in an image processing requestInterpret image processing responsesExtract text from images using Azure AI VisionConvert handwritten text using Azure AI VisionImplement custom vision modelsChoose between image classification and object detection modelsLabel imagesTrain a custom image model, including image classification and object detectionEvaluate custom vision model metricsPublish a custom vision modelConsume a custom vision modelBuild a custom vision model code firstAnalyze videosUse Azure AI Video Indexer to extract insights from a video or live streamUse Azure AI Vision Spatial Analysis to detect presence and movement of people in videoImplement natural language processing solutions (15–20%)Analyze and translate textExtract key phrases and entitiesDetermine sentiment of textDetect the language used in textDetect personally identifiable information (PII) in textTranslate text and documents by using the Azure AI Translator serviceProcess and translate speechIntegrate generative AI speaking capabilities in an applicationImplement text-to-speech and speech-to-text using Azure AI SpeechImprove text-to-speech by using Speech Synthesis Markup Language (SSML)Implement custom speech solutions with Azure AI SpeechImplement intent and keyword recognition with Azure AI SpeechTranslate speech-to-speech and speech-to-text by using the Azure AI Speech serviceImplement custom language modelsCreate intents, entities, and add utterancesTrain, evaluate, deploy, and test a language understanding modelOptimize, backup, and recover language understanding modelConsume a language model from a client applicationCreate a custom question answering projectAdd question-and-answer pairs and import sources for question answeringTrain, test, and publish a knowledge baseCreate a multi-turn conversationAdd alternate phrasing and chit-chat to a knowledge baseExport a knowledge baseCreate a multi-language question answering solutionImplement custom translation, including training, improving, and publishing a custom modelImplement knowledge mining and information extraction solutions (15–20%)Implement an Azure AI Search solutionProvision an Azure AI Search resource, create an index, and define a skillsetCreate data sources and indexersImplement custom skills and include them in a skillsetCreate and run an indexerQuery an index, including syntax, sorting, filtering, and wildcardsManage Knowledge Store projections, including file, object, and table projectionsImplement semantic and vector store solutionsImplement an Azure AI Document Intelligence solutionProvision a Document Intelligence resourceUse prebuilt models to extract data from documentsImplement a custom document intelligence modelTrain, test, and publish a custom document intelligence modelCreate a composed document intelligence modelExtract information with Azure AI Content UnderstandingCreate an OCR pipeline to extract text from images and documentsSummarize, classify, and detect attributes of documentsExtract entities, tables, and images from documentsProcess and ingest documents, images, videos, and audio with Azure AI Content Understanding