AI-102 Exam Questions & Answers
Designing and Implementing a Microsoft Azure AI Solution • Microsoft
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About AI-102 Exam
The AI-102 certification exam, officially known as Designing and Implementing a Microsoft Azure AI Solution, is a comprehensive assessment designed for professionals seeking to validate their expertise in building intelligent applications on the Azure cloud platform. This exam covers critical topics including Azure Cognitive Services, natural language processing, computer vision, knowledge mining, and machine learning model implementation. Candidates must demonstrate proficiency in designing conversational AI solutions, implementing decision-making algorithms, and leveraging Azure's advanced AI capabilities to solve real-world business problems. The certification is ideal for AI engineers, solution architects, and cloud developers who want to establish their credentials in Azure AI development and advance their career prospects in this rapidly growing field.
Success on the AI-102 exam requires thorough preparation and hands-on experience with Azure AI services. Updated exam dumps and practice tests serve as invaluable study resources, helping candidates identify knowledge gaps, familiarize themselves with the exam format, and build confidence before the actual assessment. These preparation materials provide realistic scenarios and questions that mirror the complexity of production-level AI solutions, enabling learners to practice implementing various Azure AI features in practical contexts. By combining official Microsoft documentation with quality practice tests and exam dumps, candidates can develop a comprehensive understanding of Azure AI architecture, best practices, and implementation strategies necessary to pass the certification and excel in professional AI development roles.
Exam Topics & Objectives
4-Week Study Plan for AI-102
Week 1: Knowledge Mining, Information Extraction & NLP Foundations
- Study Azure Cognitive Search architecture, indexers, and skillsets
- Learn document processing with Azure Form Recognizer and layout analysis
- Implement custom entity extraction using Text Analytics and Named Entity Recognition
- Practice building knowledge graphs with Azure Cognitive Search semantic search
- Configure OCR and handwriting recognition in Computer Vision API
- Explore Text Analytics for key phrase extraction and language detection
- Complete Microsoft Learn module on Azure Cognitive Search implementation
- Practice hands-on: Create a search solution with custom skills and knowledge extraction
Week 2: Natural Language Processing & Computer Vision Solutions
- Master Azure Language Service capabilities (sentiment analysis, entity linking, PII detection)
- Implement intent and entity recognition using LUIS or Conversational Language Understanding
- Configure question-answering solutions with QnA Maker and custom question answering
- Study Computer Vision API for image classification, object detection, and face analysis
- Learn Azure Custom Vision for training custom classification and detection models
- Implement OCR and document analysis workflows
- Practice building chatbots with Language Understanding and Bot Framework
- Complete labs: NLP classification, sentiment analysis pipeline, and custom vision model training
Week 3: Generative AI, Agents & Advanced Solutions
- Study Azure OpenAI Service deployment and API integration
- Learn prompt engineering best practices and prompt templates
- Implement retrieval-augmented generation (RAG) with Cognitive Search and OpenAI
- Explore semantic kernel for agentic solutions and multi-step reasoning
- Configure Azure AI Foundry for model orchestration and experimentation
- Study content filtering, safety measures, and responsible AI implementation
- Learn agentic patterns: tool use, function calling, and autonomous decision-making
- Practice: Build a RAG solution and an agent with tool integration
Week 4: Planning, Management & Exam Preparation
- Study Azure AI solution architecture patterns and design principles
- Learn cost optimization, monitoring, and performance tuning strategies
- Understand authentication, security, and compliance in AI solutions
- Practice solution planning: requirements gathering, resource selection, scalability
- Review Azure AI ethical guidelines and responsible AI principles
- Study disaster recovery, backup, and business continuity planning
- Take full-length practice exams and identify weak areas
- Review all hands-on labs and practice scenario-based questions covering all 6 domains
Sample AI-102 Questions
Practice with real exam-style questions. Reveal answers to verify your knowledge.
You are building an app that will use Azure AI to monitor workspaces for safety regulation compliance.
You need to recommend a service that meets the following requirements:
Generates alerts when employees enters high-risk areas
Monitors video feeds in real time
Minimizes development effort
What should you recommend?
You have an Azure subscription that contains an Azure OpenAI resource.
You plan to build an agent by using the Azure Ai Agent Service. The agent will perform the following actions:
* Interpret written and spoken questions from users.
* Generate answers to the questions.
* Output the answers as speech.
You need to create the project for the agent.
What should you use?
Your company wants to reduce how long it takes for employees to log receipts in expense reports. All the receipts are in English.
You need to extract top-level information from the receipts, such as the vendor and the transaction total. The solution must minimize development effort.
Which Azure Cognitive Services service should you use?
You have a collection of 50,000 scanned documents that contain text.
You plan to make the text available through Azure Cognitive Search.
You need to configure an enrichment pipeline to perform optical character recognition (OCR) and text analytics. The solution must minimize costs.
What should you attach to the skillset?
You have an Azure subscription.
You plan to build an app that will automate complex workflows and enable collaboration among specialized agents.
You need to recommend a technical approach for building the app. The solution must meet the following requirements:
* Be flexible and extensible.
* Support agent collaboration, tool integration, and memory management.
What should you recommend?
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