Microsoft AI-102 Exam Syllabus & Study Guide

At Certs4Success, we provide the most accurate and up-to-date preparation materials for the Microsoft AI-102 Exam. Our content is professionally designed to help you master all the essential topics required to pass the Microsoft AI-102 Exam with confidence and build advanced AI solutions using Microsoft Azure. If you are planning to clear the Designing and Implementing a Microsoft Azure AI Solution Microsoft AI-102 Exam, this detailed syllabus will guide you through all the important domains you need to focus on.

All Exam Topics of Microsoft AI-102 Exam

Topic 1: Plan and Manage an Azure AI Solution

AI Workloads: Identifying appropriate AI workloads and Azure services.
Resource Management: Creating and managing Azure AI resources.
Monitoring: Implementing logging and monitoring for AI solutions.

Topic 2: Implement Decision Support Solutions

Azure AI Language: Building text analytics and language understanding solutions.
Azure AI Search: Implementing intelligent search experiences.
Document Intelligence: Extracting data from forms and documents.

Topic 3: Implement Computer Vision Solutions

Image Analysis: Using Azure AI Vision for image recognition and tagging.
Optical Character Recognition (OCR): Extracting text from images.
Face Detection: Implementing face recognition solutions responsibly.

Topic 4: Implement Natural Language Processing (NLP) Solutions

Text Analytics: Performing sentiment analysis and key phrase extraction.
Language Understanding: Building conversational language models.
Translation: Implementing multilingual solutions.

Topic 5: Implement Knowledge Mining Solutions

Data Ingestion: Importing structured and unstructured data.
Enrichment Pipelines: Enhancing data with AI skills.
Indexing: Creating searchable knowledge stores.

Topic 6: Implement Generative AI Solutions

Azure OpenAI Service: Creating generative AI applications.
Prompt Engineering: Designing effective prompts for AI models.
Content Generation: Building chatbots and content creation tools.

Topic 7: Implement Conversational AI Solutions

Bot Development: Creating bots using Azure Bot Service.
Dialog Management: Designing conversational flows.
Channel Integration: Connecting bots to multiple platforms.

Topic 8: Implement Responsible AI Practices

Ethical AI: Ensuring fairness, transparency, and accountability.
Data Privacy: Protecting sensitive data in AI solutions.
Compliance: Following regulatory standards and guidelines.

Topic 9: Integrate AI Solutions with Applications

API Integration: Connecting AI services with applications.
SDK Usage: Using SDKs for development and deployment.
Automation: Streamlining workflows using AI services.

Topic 10: Deployment, Monitoring & Optimization

Deployment Strategies: Publishing AI solutions to production.
Performance Tuning: Optimizing models and services.
Troubleshooting: Identifying and resolving issues efficiently.

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