Study Guide

Designing and Implementing a Microsoft Azure AI Solution Study Guide

Use the saved domain outline to connect plan and manage an azure ai solution, implement knowledge mining and azure ai search solutions, implement generative ai solutions, implement natural language processing solutions to scenario-based questions and explanations.

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How the Exam Is Structured

Designing and Implementing a Microsoft Azure AI Solution (AI-102) validates plan and manage an azure ai solution, implement knowledge mining and azure ai search solutions, implement generative ai solutions, implement natural language processing solutions. The ExamPal practice bank includes 100 premium questions and 40 free questions mapped across the official blueprint.

Domain	Weight	Focus
Domain 1: Plan and Manage an Azure AI Solution	24%	Task 1.1: Select and provision Azure AI services; Choose appropriate Azure AI services
Domain 2: Implement Knowledge Mining and Azure AI Search Solutions	20%	Task 2.1: Design an Azure AI Search index solution; Create and manage indexing components
Domain 3: Implement Generative AI Solutions	22%	Task 3.1: Build solutions with Azure OpenAI models; Select appropriate model types
Domain 4: Implement Natural Language Processing Solutions	14%	Task 4.1: Analyze and understand text with Azure AI Language; Detect text features
Domain 5: Implement Computer Vision and Document Intelligence Solutions	12%	Task 5.1: Analyze images and video with Azure AI Vision; Generate visual insights
Domain 6: Implement Agentic Solutions	8%	Task 6.1: Design agent-based AI solutions; Identify appropriate agentic use cases

24% of exam

Domain 1: Plan and Manage an Azure AI Solution

Covers planning, provisioning, securing, monitoring, governing, and optimizing Azure AI solutions across supported service types and deployment environments. This domain also includes responsible AI and governance practices needed for production AI workloads.

Task 1.1: Select and provision Azure AI services

Choose appropriate Azure AI services

Select pricing tiers, regions, and deployment options

Differentiate resource and platform capabilities

Plan for quotas and service limits

Task 1.2: Secure Azure AI solutions

Implement authentication methods

20% of exam

Domain 2: Implement Knowledge Mining and Azure AI Search Solutions

Covers designing, enriching, extending, querying, and consuming Azure AI Search solutions for enterprise knowledge mining. This domain includes indexing, cognitive enrichment, vector and semantic retrieval, and search experience design.

Task 2.1: Design an Azure AI Search index solution

Create and manage indexing components

Define searchable and retrievable fields

Configure ranking and analysis features

Select appropriate search tiers

Task 2.2: Build enrichment pipelines for unstructured content

Use built-in cognitive skills

22% of exam

Domain 3: Implement Generative AI Solutions

Covers building, grounding, evaluating, filtering, and orchestrating generative AI solutions. This domain emphasizes Azure OpenAI model usage, RAG, prompt engineering, safety, and workflow orchestration.

Task 3.1: Build solutions with Azure OpenAI models

Select appropriate model types

Configure production deployments

Structure prompts with roles

Control generation parameters

Task 3.2: Implement retrieval-augmented generation (RAG)

Ground responses with enterprise data

14% of exam

Domain 4: Implement Natural Language Processing Solutions

Covers text analysis, conversational language understanding, question answering, document-grounded chat, translation, and speech-enabled NLP. This domain focuses on Azure AI Language, CLU, Translator, and speech capabilities.

Task 4.1: Analyze and understand text with Azure AI Language

Detect text features

Use appropriate text analytics features

Apply PII detection and redaction

Interpret confidence and limitations

Task 4.2: Build conversational language understanding solutions

Create CLU projects

12% of exam

Domain 5: Implement Computer Vision and Document Intelligence Solutions

Covers image, video, OCR, custom vision, and document intelligence solutions. This domain includes extracting insights from visual content and structured data from business documents.

Task 5.1: Analyze images and video with Azure AI Vision

Generate visual insights

Classify visual content

Select image or video features

Interpret confidence and optimize input

Task 5.2: Extract text from visual content

Use OCR and Read

8% of exam

Domain 6: Implement Agentic Solutions

Covers designing, implementing, controlling, evaluating, and securing agent-based AI systems. This domain focuses on when to use agents, how to orchestrate tools and actions, and how to govern execution safely.

Task 6.1: Design agent-based AI solutions

Identify appropriate agentic use cases

Define agent components

Design agent patterns

Balance autonomy and control

Task 6.2: Implement tools and orchestration for agents

Connect agents to enterprise systems

Key Terms to Know

These terms are loaded from the shared terminology pack and appear across the question explanations.

Azure AI Custom Vision Object Detection: A computer vision capability used to identify and locate objects in images by returning bounding boxes for each detected instance.
Azure AI Video Indexer: A service that analyzes video and audio content to generate searchable insights such as transcripts, speakers, topics, and brand mentions.
Azure Translator Document Translation API: An Azure translation feature used to translate entire documents while preserving their original formatting and structure.
BLEU score: A machine translation evaluation metric that compares generated translations against reference translations.
Basic tier: An Azure AI Search pricing tier that does not support certain advanced capabilities such as semantic search.
Bounding box: A rectangular coordinate region that marks the location of a detected object within an image.
Brand mention detection: The process of identifying references to company or product brands within media content.
CLU: Conversational Language Understanding, an Azure service used to detect intents and extract entities from user utterances.
Custom Neural model: A custom document intelligence model trained to extract fields from documents with varied layouts and formats.
Custom Speech: An Azure Speech capability that improves speech recognition accuracy for domain-specific vocabulary by training with custom data.
Custom Translator: A feature for training translation models with domain-specific bilingual data to improve terminology and translation quality.
Custom Web API skill: A skill in an Azure AI Search skillset that calls an external REST endpoint during document enrichment.
Embedding field: A vector-based field in a search index that stores numerical representations of content for similarity search.
File projection: A knowledge store projection type used to store binary outputs such as images extracted during enrichment.
Groundedness: An evaluation measure indicating whether a generated response is supported by and faithful to the provided source content.
Intent: The goal or purpose a user is expressing in a natural language request.
Knowledge store: A persistence layer in Azure AI Search that stores enriched content generated during indexing for downstream analysis.
OCR: Optical Character Recognition, a process that extracts readable text from images or scanned documents.

Official Materials and Guidance

This page is built from Microsoft official materials and ExamPal shared release pack, the shared syllabus, topic tree, terminology pack, free pack, and premium pack.

-Guidance: Microsoft Learn study guide, practice assessment, sandbox, prep videos
-Domain outline: Plan/manage Azure AI solution 20-25%; GenAI solutions 15-20%; Agentic solution 5-10%; Computer vision 10-15%; NLP 15-20%; Knowledge mining/extraction 15-20%.

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