Coverage Matrix

This matrix maps every official AI-102 exam skill to the challenges that cover it. Use it to identify gaps in your preparation and ensure complete coverage.

How to use this

1. Check off skills as you complete challenges
2. If you're short on time, prioritize domains by weight percentage
3. Use this as a final review checklist before exam day

Domain 1: Plan and Manage an Azure AI Solution (20–25%)

Skill	Challenges	Key Topics
Select the appropriate Azure AI service	01, 02	Service selection criteria, multi-service vs single-service
Plan and configure security for Azure AI services	03, 04	Keys, RBAC, managed identity, network security
Create and manage an Azure AI service resource	01, 02, 05	Portal, CLI, Bicep/ARM provisioning
Configure diagnostic logging	06	Azure Monitor, Log Analytics, diagnostic settings
Manage costs for Azure AI services	07	Pricing tiers, budgets, cost analysis
Monitor Azure AI services	06, 08	Metrics, alerts, health checks
Implement responsible AI practices	09, 10	Content filtering, transparency, fairness, governance
Deploy AI services in containers	05	Docker, connected/disconnected containers, billing
Manage keys and secure endpoints	03, 04	Key rotation, Key Vault integration, private endpoints
Plan and implement a virtual network	04	VNet integration, private endpoints, service endpoints

Domain 2: Implement Generative AI Solutions (15–20%)

Skill	Challenges	Key Topics
Create an Azure AI Foundry project	11	AI Foundry portal, project setup, connections
Select and deploy Azure OpenAI models	12, 13	GPT-4o, embedding models, deployment configurations
Implement RAG (Retrieval-Augmented Generation)	14, 15	Chunking, embeddings, vector search, grounding
Implement prompt engineering	16, 17	System prompts, few-shot, chain-of-thought, temperature
Configure content filtering	18	Severity levels, custom filters, blocklists
Implement Azure OpenAI on your data	14, 15	Data sources, indexing, citation handling
Generate code and images with Azure OpenAI	19	DALL-E, code generation, function calling
Implement orchestration flows	20	Prompt flow, LangChain, Semantic Kernel
Manage token usage and rate limits	12, 13	TPM, RPM, quotas, retry strategies
Evaluate generative AI responses	17, 20	Groundedness, relevance, coherence metrics

Domain 3: Implement AI Agent Solutions (5–10%)

Skill	Challenges	Key Topics
Design agent architecture	21	Agent components, planning, memory, tools
Implement tool use and function calling	22	Tool definitions, parallel tool calls, response handling
Implement multi-agent orchestration	23	Agent collaboration, handoffs, Semantic Kernel agents

Domain 4: Implement Computer Vision Solutions (10–15%)

Skill	Challenges	Key Topics
Analyze images using Azure AI Vision	24, 25	Image Analysis 4.0, captions, tags, objects, people
Implement custom image classification	26	Custom Vision training, iteration, publishing
Implement custom object detection	27	Bounding boxes, training data, evaluation
Read text from images and documents (OCR)	28	Read API, handwriting, multi-language
Implement face detection and analysis	29	Face API, attributes, verification, identification
Analyze video content	30	Video Indexer, scene detection, transcription

Domain 5: Implement Natural Language Processing Solutions (15–20%)

Skill	Challenges	Key Topics
Analyze text (sentiment, entities, key phrases)	31, 32	TextAnalyticsClient, batch operations
Detect and redact PII	33	PII categories, redaction, domain filters
Translate text and documents	34	Translator API, custom translator, document translation
Implement speech-to-text	35	Real-time recognition, batch transcription, custom models
Implement text-to-speech	36	Neural voices, SSML, custom voice
Implement Conversational Language Understanding (CLU)	37	Intents, entities, training, deployment
Implement Custom Question Answering	38	Knowledge bases, multi-turn, active learning
Implement speech translation	39	Real-time translation, multi-language

Domain 6: Implement Knowledge Mining and Document Intelligence (15–20%)

Skill	Challenges	Key Topics
Create and manage Azure AI Search indexes	40, 41	Index schema, fields, analyzers, scoring profiles
Implement an indexing pipeline	42, 43	Indexers, data sources, change detection
Implement AI enrichment with skillsets	44, 45	Built-in skills, custom skills, knowledge store
Implement vector search	46	Vector fields, HNSW, hybrid search
Query an Azure AI Search index	47	Simple/full Lucene syntax, filters, facets
Analyze documents with Document Intelligence	48	Prebuilt models, custom models, composed models

Domain 7: Capstone

Skill	Challenge	Key Topics
End-to-end AI solution integration	49	Combining services, production patterns, monitoring

Coverage Summary

Domain	Weight	Challenges	Total
Plan and manage	20–25%	01–10	10
Generative AI	15–20%	11–20	10
AI agents	5–10%	21–23	3
Computer vision	10–15%	24–30	7
NLP	15–20%	31–39	9
Knowledge mining & docs	15–20%	40–48	9
Capstone	—	49	1
Total	100%	01–49	49

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Identify gaps? Jump to the relevant domain's first challenge to fill them.