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Microsoft Course Directory

AI-102: Designing and Implementing a Microsoft Azure AI Solution

Microsoft

Microsoft Course Directory

AI-102: Designing and Implementing a Microsoft Azure AI Solution

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Course Name	Course Type	Syllabus
Azure AI Engineer Associate: AI Enrichments Knowledge Stores and Apache Lucene	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: AI Enrichments Knowledge Stores and Apache Lucene Overview/Description: AI Enrichments in Azure AI Search is a feature that converts non-text content like images and videos into searchable text using skillsets composed of AI-powered skills. In this course, discover AI enrichment pipelines that incorporate language, vision, and speech services, and implement OCR and image analysis to generate searchable descriptions. Next, learn about the knowledge store, which is a separate structure from the search index and supports downstream analysis using projections like tables, objects, and files. Finally, explore Apache Lucene and apply advanced search techniques like Lucene full syntax, boosting and ordering, facets, proximity, fuzzy, and wildcard search. This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_16_enus
Azure AI Engineer Associate: Azure AI Content Understanding	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Azure AI Content Understanding Overview/Description: Azure AI Content Understanding, launched in public preview at Microsoft Ignite in November 2024, extends the capabilities of Azure AI Document Intelligence to include images, audio, and video.In this course, explore how Azure AI Content Understanding uses generative AI to transform unstructured content such as documents, images, audio, and video into structured, usable data. Next, learn how to use analyzers for content and field extraction, going beyond basic structure to generate summaries and do a lot more. Finally, discover how to configure tasks by extracting, classifying, and generating fields across multiple data types and modalities.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_18_enus
Azure AI Engineer Associate: Azure AI Document Intelligence	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Azure AI Document Intelligence Overview/Description: Azure AI Document Intelligence is focused on processing complex documents. Formerly known as Azure Form Recognizer, this service works with Azure AI Content Understanding to boost business automation at scale. In this course, explore where Azure AI Document Intelligence fits within the Azure AI suite and how it compares to services like Vision and Content Understanding. Next, discover core capabilities through OCR, layout, and prebuilt models for invoices and IDs, along with features such as high-res OCR, PDF generation, and key-value extraction. Finally, learn how to build custom models, compare neural and template approaches, and apply composed models.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_17_enus
Azure AI Engineer Associate: Azure AI Foundry and Language Studio	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Azure AI Foundry and Language Studio Overview/Description: Azure Language Studio is a no-code/low-code tool for building, customizing, and deploying natural language processing (NLP) solutions. Azure AI Foundry is a platform designed to help businesses build, train, and deploy AI models at scale.In this course, explore the basics of Azure Language Studio and Azure AI Foundry. Next, learn about the hierarchical structure of hubs and projects in Azure AI Foundry, create a hub within it, view connected resources, and use the AI Foundry Language playground to perform NLP tasks. Finally, discover how to provision a Language resource instance from the Azure Portal to use in Language Studio and AI Foundry.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_02_enus
Azure AI Engineer Associate: Azure OpenAI and AI Foundry	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Azure OpenAI and AI Foundry Overview/Description: Implementing generative AI solutions is a significant portion of the AI-102 exam, so its important to master the use of the Azure OpenAI service, the cloud-based platform that provides access to advanced AI models.In this course, learn about Azure OpenAI and AI Foundry, as well as the Azure AI Foundry nomenclature, workflows, and project types. Next, compare the strengths and weaknesses of AI Foundry and Hub-based projects and explore the relationship between Azure AI Foundry and Azure OpenAI. Finally, learn how to use the model catalog in AI Foundry to discover models and deploy those models in a project.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_08_enus
Azure AI Engineer Associate: Configuring and Using Azure AI Search	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Configuring and Using Azure AI Search Overview/Description: Getting the best out of Azure AI Search involves understanding full-text search in detail, but far more important these days is vector search. It is vector search that allows user queries to be matched with results that are similar in meaning – even if the results are in entirely different languages, and even in different modalities such as video or images. Begin this course by exploring the indexing and querying processes – this will involve provisioning an Azure AI Search resource, configuring the data source and indexer, editing the schema, and then querying the index once it is created. Learn about the Best Match 25 (BM25) algorithm and a powerful feature known as semantic reranking. Next, discover vector-search and vectorizing data using an embeddings model. Finally, learn about scaling Azure AI Search by balancing the number of replicas and partitions while meeting constraints on the number of search units, and mastering the use-cases of different search tiers.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_15_enus
Azure AI Engineer Associate: Conversational Language & Custom Question Answering	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Conversational Language & Custom Question Answering Overview/Description: Azure AI Language resources offer two great features for building intelligent conversational experiences. Conversational language understanding enables you to build custom models to predict user input intention, and custom question answering allows you to provide conversational question-and-answer experiences.In this course, learn how to implement conversational language understanding, add intents and entities, and prepare training data before training, deploying, and consuming CLU models. Next, explore custom question answering, set up knowledge bases, and view QnA pair structure. Finally, discover how to implement custom question answering, configure and deploy a project in Language Studio, and enhance projects by adding QnA pairs and chitchat.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_04_enus
Azure AI Engineer Associate: Fine-Tuning and Model Evaluation	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Fine-Tuning and Model Evaluation Overview/Description: There are situations where domain knowledge becomes so important that it must be incorporated into the model itself. Azure AI Foundry has excellent support for this and allows for the thorough evaluation of models using metric, simulator, and red teaming evaluations.In this course, explore the use cases and best practices for fine-tuning and situations where fine-tuning is the wrong choice. Next, discover how to fine-tune models in Azure AI Foundry and the use cases and methods of evaluation. Finally, learn how to evaluate an LLM using another model and how to use AI Foundry to run the evaluation and report the results.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_12_enus
Azure AI Engineer Associate: Image Analysis in the Azure AI Vision Service	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Image Analysis in the Azure AI Vision Service Overview/Description: Assessing how to implement computer vision solutions is an important part of the AI-102 exam. The Azure AI Vision, AI Custom Vision, and Video Indexer services, as well as Vision Studio, are all key elements of this domain.In this course, explore Azure AI Vision and Vision Studio, the Image Analysis service, and other Azure AI offerings. Next, learn how to use the Image Analysis service for optical character recognition (OCR) and captioning and perform image object detection, tag extraction, and thumbnail generation. Finally, examine the capabilities of the Image Analysis v3.2 API and how to use its extended features.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_06_enus
Azure AI Engineer Associate: Implementing AI Solutions Responsibly	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Implementing AI Solutions Responsibly Overview/Description: Previously, the AI-102 exam included a domain on implementing AI solutions responsibly, which has now been merged into the domain on planning and managing an Azure AI solution. Despite this change, the topic remains vital, especially with new capabilities in Azure AI Content Safety.In this course, explore the features of the Content Safety service using the SDK with ContentSafetyClient and AnalyzeTextOptions. Next, discover how to perform text and image moderation tests in Azure AI Foundry using filters and blocklists. Finally, learn about advanced tools such as prompt shields, groundedness detection for Q&A and summarization, and protected material detection for code and text.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_21_enus
Azure AI Engineer Associate: Introducing Azure AI Search	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Introducing Azure AI Search Overview/Description: The starring role in the AI-102 exams "Implementing Knowledge Mining and Information Extraction Solutions" domain belongs to Azure AI Search. This service, which has had several names in the past, most recently Azure Cognitive Search, is the second-most important service in the AI-102 curriculum, after Azure OpenAI. Both those services are key in implementing RAGs, and Azure AI Search provides the vector search capabilities that make such architectures possible.In this course, explore full-text search and the inverted index, as well as vector search and the intuition behind embeddings. Discover Azure AI Search – its history and evolution, competitors and use-cases, and the reasons for its prominence and success today. Delineate the parts played by the index, indexer, and data source, as well as the importance of the schema of the index. Finally, learn how to work with the schema of the JSON and distinguish between attributes such as searchable, sortable, facetable, filterable and retrievable.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_14_enus
Azure AI Engineer Associate: Introducing Azure AI Services	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Introducing Azure AI Services Overview/Description: The Microsoft Certified AI-102 Azure AI Engineer Associate certification is an intermediate-level certification that validates your ability to build, manage, and deploy AI solutions using Azure AI services.In this course, explore the most critical AI technologies available through Microsoft Azure. Next, learn about the Azure AI suite of services and key Azure AI technologies, including Azure OpenAI, Azure AI Search, and Azure AI Foundry. Finally, examine the AI-102 exams learning objectives, format, and valuable test-taking tips and strategies.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_01_enus
Azure AI Engineer Associate: Language Tasks Using REST APIs and Client SDKs	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Language Tasks Using REST APIs and Client SDKs Overview/Description: Azure AI Foundry and Language Studio offer interactive ways to perform natural language processing (NLP) on Azure. For developers looking for programmatic access, the client SDKs and REST APIs are an ideal choice. In this course, you will explore Azure AI Language programmatic access, differentiate between prebuilt and custom Language service capabilities, and connect to Language and AI services resource objects. Next, learn how to perform key phrase extraction in multiple ways, perform named entity recognition (NER) programmatically and asynchronously, and detect PII in text. Finally, discover how to perform sentiment analysis, use opinion mining to extract granular text insights, and examine custom model capabilities. This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_03_enus
Azure AI Engineer Associate: Model Parameters and Prompt Engineering	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Model Parameters and Prompt Engineering Overview/Description: While building generative AI apps, it can be a challenge to shape the model responses to be in the right voice and structure. Prompt engineering is another vital skill that enables in-context learning, where the model learns from the prompts without any retraining.In this course, learn about Azure AI model parameters and the roles of the six model parameters of OpenAI models. Next, examine what prompt engineering is and how to utilize it to enhance model output and reduce costs. Finally, discover how to perform prompt engineering techniques, including few-shot, chain-of-thought, and role-based prompting.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_10_enus
Azure AI Engineer Associate: Projects Playgrounds and Models	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Projects Playgrounds and Models Overview/Description: Building generative AI apps on Azure generally involves using Azure AI Foundry to pick a model, deploying that model, prototyping and experimenting with it from a playground, and programmatically accessing deployment and generating code with client SDKs.In this course, learn how to create and deploy AI Foundry projects and use OpenAI models with playgrounds and SDKs. Next, examine how to use the Chat SDK for model authentication and code generation and use Assistants to enable context-aware AI interactions. Finally, discover the OpenAI model types available in AI Foundry, use an image generation model for Hub-based project creation, and use the images playground to generate images.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_09_enus
Azure AI Engineer Associate: Retrieval Augmented Generation	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Retrieval Augmented Generation Overview/Description: Retrieval Augmented Generation (RAG) refers to the architecture where an LLM is paired with a real-time retrieval system. Azure AI and AI Foundry have solid support for RAG, relying heavily on the Azure OpenAI and Azure AI Search services.In this course, learn how RAG systems work and the importance of embeddings. Next, examine the key Azure AI services needed for a RAG system and how to use RAG to enhance AI model responses. Finally, discover how to set up an Azure AI search index, build a data retrieval vector database and search index, and implement a RAG solution for information retrieval.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_11_enus
Azure AI Engineer Associate: Securing and Monitoring Azure AI Services	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Securing and Monitoring Azure AI Services Overview/Description: The skill area on planning and managing an Azure AI solution makes up 20E5% of the AI-102 exam and focuses on security, authentication, monitoring, logging, and deployment.In this course, explore various authentication methods such as API keys, short-lived tokens, and Microsoft Entra ID with role-based access control. Next, discover how to configure metrics, alerts, and use Kusto Query Language (KQL) with Log Analytics for monitoring. Finally, learn how to containerize and deploy Azure AI models via Microsoft Artifact Registry, ensure billing endpoints are set at deployment, and handle offline deployment with Microsoft’s approval.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_20_enus
Azure AI Engineer Associate: Tracing and Prompt Flows	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Tracing and Prompt Flows Overview/Description: Prompt flows and tracing are powerful Azure AI Foundry tools. Tracing is crucial for debugging complex applications and identifying performance bottlenecks, and prompt flows visually orchestrate executable workflows to help you easily build GenAI apps.In this course, explore use cases for tracing in Azure AI and how traces and spans can be used for end-to-end system observability. Next, learn about the structure, usage, and flaws of prompt flows and how to use the tools to enhance prompt flows. Finally, discover how to build an AI Foundry prompt flow and a chat flow that summarizes web content.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_13_enus
Azure AI Engineer Associate: Translation & Speech Tasks	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Translation & Speech Tasks Overview/Description: Azure AI offers a suite of services that enable you to build comprehensive multilingual AI solutions. Azure Translator enables real-time and batch text and document translation, and Azure Speech converts audio into text and text into speech.In this course, learn how to work with Azure AI Translator, create a Translator resource, and use the Python SDK and REST API for various tasks. Next, explore Azure AI Speechs prebuilt features, custom speech models, and how to work with text-to-speech using Azure AI Foundry and the Python SDK. Finally, discover how to generate speech using SSML and convert speech-to-text with the Python SDK.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_05_enus
Azure AI Engineer Associate: Using the Azure AI Foundry Agent Service	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Using the Azure AI Foundry Agent Service Overview/Description: AI agents are gaining attention for their powerful action-taking abilities, filled with both promise and risk. There is a growing realization that this technology could truly be revolutionary.In this course, explore how agents combine large language models (LLMs), instructions, and tools to perform tasks. Next, discover how they push AI beyond chatbots and assistants, and learn about the Azure AI Agent Service, its tools, and how Basic and Standard projects differ. Finally, learn how to choose between connected agents, Semantic Kernel, and Autogen for multi-agent workflows. This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_19_enus
Azure AI Engineer Associate: Video Indexer and Custom Vision	Course	View details Course Syllabus \| Print Syllabus Azure AI Engineer Associate: Video Indexer and Custom Vision Overview/Description: Azure AI has a suite of services for vision-related tasks. While Azure AI Vision is the flagship service, other services such as Spatial Analysis, Face, Video Indexer, and Custom Vision are also significant.In this course, learn about the Azure AI Spatial Analysis and Video Indexer services and compare the capabilities of both. Next, explore the audio and video capabilities of Video Indexer and the capabilities of the Custom Vision service. Finally, discover how to train and evaluate Custom Vision models and train an image classification model using the Custom Vision service.This course is part of a collection that prepares learners for the Designing and Implementing a Microsoft Azure AI Solution (AI-102) exam. Course Number: it_clazae25_07_enus