AI-102 Designing and Implementing a Microsoft Azure AI Solution Exam Topics and Questions
These Microsoft Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam topics are organized according to official exam domains to help candidates quickly verify coverage and focus on assessment rather than theory. Each domain is paired with topic-wise AI-102 sample questions that reflect how objectives are tested in the actual exam. This structure enables efficient review, targeted self-assessment, and rapid identification of weak areas when preparing for the Microsoft Designing and Implementing a Microsoft Azure AI Solution certification exam.
Let's Practice Free Microsoft AI-102 Questions Aligned with Official Exam Topics
Exam Contains: 6 Topics
Topic Content
Implement knowledge mining and information extraction solutions by mastering Azure AI Search, which involves provisioning resources, creating indexes, defining skillsets, establishing data sources, and building indexers to enable advanced querying with sorting, filtering, and wildcard capabilities while managing Knowledge Store projections and implementing semantic and vector search solutions. Additionally, develop expertise in Azure Document Intelligence by provisioning resources, utilizing prebuilt models for data extraction, creating and training custom models, testing their performance, and composing multiple models for complex document processing...
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Sample Questions for Topic 1 : Implement knowledge mining and information extraction solutions
Q1
You are composing multiple custom models in Azure Document Intelligence to handle complex document processing scenarios. What is the key advantage of this approach?
Topic Content
Implement Natural Language Processing Solutions
This exam topic covers the comprehensive implementation of NLP capabilities across multiple dimensions. Candidates must master text analysis and translation techniques, including extracting key phrases and entities, determining text sentiment, detecting languages, identifying personally identifiable information (PII), and translating documents using Azure Translator services. Additionally, they should understand speech processing and translation, integrating generative AI speaking capabilities, implementing text-to-speech and speech-to-text conversions, enhancing output with Speech Synthesis Markup Language (SSML), and enabling custom speech solutions with...
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Topic Content
Implement Computer Vision Solutions
This exam topic covers the implementation of comprehensive computer vision solutions across multiple domains. Candidates must master image analysis by selecting appropriate visual features for processing requirements, detecting objects, generating tags, and interpreting responses from image analysis services. Text extraction and handwriting recognition using Azure Vision tools are essential skills. The topic also requires expertise in building custom vision models, including choosing between image classification and object detection approaches, labeling training data, training models, evaluating performance metrics,...
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Topic Content
Create custom agents by first understanding the fundamental role and practical use cases of agents in modern application development. Configure all necessary resources and infrastructure required to build a functional agent system. Create agents using the Microsoft Foundry Agent Service, which provides the core capabilities for agent development and deployment. Implement more complex agents leveraging the Microsoft Agent Framework to handle advanced scenarios and sophisticated logic. Design and implement complex workflows that support multi-agent orchestration, handle multiple concurrent users, and...
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Topic Content
Build generative AI solutions with Microsoft Foundry by planning and preparing for implementation, then deploying hubs, projects, and necessary resources while selecting appropriate models for your specific use case. You'll implement prompt flow solutions, establish RAG patterns to ground models in your data, evaluate performance, and integrate projects into applications using the Microsoft Foundry SDK with prompt templates. Leverage Azure OpenAI in Foundry Models to generate content by provisioning resources, deploying models, submitting prompts for code and natural language responses,...
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Topic Content
Select the appropriate Microsoft Foundry Services for your Azure AI solution by identifying the right service for each use case: generative AI solutions, computer vision applications, natural language processing tasks, speech recognition and synthesis, information extraction requirements, and knowledge mining initiatives. Plan, create, and deploy Microsoft Foundry Services by designing solutions aligned with Responsible AI principles, creating Azure AI resources, selecting appropriate AI models, deploying models through suitable deployment options, integrating SDKs and APIs, configuring default endpoints, incorporating services into...
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