Azure AI-102 study guide

Azure AI-102 · Work through your exam, one topic at a time.

AI-102 is the exam behind the Microsoft Certified: Azure AI Engineer Associate credential, an intermediate-level, Azure-role certification with an artificial intelligence subject focus. It targets engineers who build, manage, and deploy AI solutions on Azure - people who work across requirements definition and design, development, deployment, integration, maintenance, performance tuning, and monitoring, and who collaborate with solution architects, data scientists, data engineers, IoT specialists, and infrastructure administrators to deliver complete, secure end-to-end AI solutions. The Azure AI-102 study guide has 6 sections and about 2,080 words.

Plan and Manage an Azure AI Solution

A multi-service Azure AI Services resource gives you one endpoint and one key set for many services, which simplifies billing and code - but not every service is supported by the shared key, and some workloads (custom model training, certain vision or speech capabilities) expect a single-service resource. Region availability, pricing tier (F0 free vs S0 standard, one F0 per subscription per service type) and quota limits are exam favourites.

377 words, about 2 min 02

Implement Generative AI Solutions

In Azure OpenAI, you create a resource, then deploy a model. The deployment name is what code sends as model ; the underlying model name may differ, and this is the single most common exam trap.

346 words, about 2 min 03

Implement an Agentic Solution

An agent in Azure AI Foundry Agent Service is a model plus instructions , tools and optional knowledge. Runtime objects are: thread (durable conversation state, stored service-side), message (user or assistant content, files), and run (one execution of the agent against a thread).

342 words, about 2 min 04

Implement Computer Vision Solutions

Azure AI Vision Image Analysis returns captions, dense captions, tags, objects with bounding boxes, people, smart crops and text. OCR (the Read model) extracts printed and handwritten text as lines and words with bounding polygons; it is asynchronous for larger documents and returns a readResults / analyzeResult structure you must parse.

332 words, about 2 min 05

Implement Natural Language Processing Solutions

Language detection returns a language code and score. Sentiment analysis gives document-level and sentence-level scores for positive, neutral and negative plus the dominant label; documents containing both strong positive and negative sentences return mixed .

330 words, about 2 min 06

Implement Knowledge Mining and Information Extraction Solutions

Data source → indexer → skillset → index (and optionally a knowledge store). An indexer crawls a supported source (Blob Storage, Azure SQL, Cosmos DB, ADLS), applies a skillset of enrichment skills (OCR, key phrases, entities, custom models, embeddings), and writes fields into the index.

353 words, about 2 min