Microsoft Certified: Azure AI Engineer Associate (AI-102) Practice Exams & Study Guide

Select appropriate Microsoft Foundry Services, plan and create deployments, manage and monitor resources, and implement AI solutions responsibly. Includes selecting services for generative AI, computer vision, NLP, speech, information extraction, and knowledge mining solutions. Plan for Responsible AI principles, deploy AI models, integrate into CI/CD pipelines, monitor resources, manage costs, and implement content moderation.

Build generative AI solutions with Microsoft Foundry, use Azure OpenAI in Foundry Models to generate content, and optimize and operationalize generative AI solutions. Includes deploying hubs and projects, implementing RAG patterns, using prompt flows, evaluating models, integrating with applications, using DALL-E for image generation, configuring parameters, monitoring, optimization, and fine-tuning models.

Create custom agents using Microsoft Foundry Agent Service and Microsoft Agent Framework. Includes understanding agent roles and use cases, configuring resources, implementing complex agents and workflows with orchestration for multi-agent solutions, and testing, optimizing, and deploying agents.

Analyze images, implement custom vision models, and analyze videos. Includes selecting visual features, detecting objects, extracting text from images, converting handwritten text, training custom image classification and object detection models, using Azure AI Video Indexer, and implementing spatial analysis for video.

Analyze and translate text, process and translate speech, and implement custom language models. Includes extracting key phrases and entities, sentiment analysis, language detection, PII detection, text translation, text-to-speech and speech-to-text, custom speech solutions, intent recognition, language understanding models, question answering, and custom translation models.

Implement Azure AI Search solutions, Azure Document Intelligence in Foundry Tools solutions, and extract information with Azure Content Understanding in Foundry Tools. Includes provisioning resources, creating indexes and skillsets, implementing custom skills, querying indexes, managing Knowledge Store, implementing semantic and vector solutions, using prebuilt and custom document intelligence models, and creating OCR pipelines for document processing.