Azure AI Vision vs Azure AI Search vs Microsoft Foundry: Complete AB-731 Exam Guide

Last Updated: February 2026

If you're preparing for the AB-731 (Microsoft AI Transformation Leader) exam, understanding Azure AI services is essential. With 35-40% of exam questions focused on identifying the right AI service for business scenarios, this guide breaks down the three core services you need to master: Azure AI Vision, Azure AI Search, and Microsoft Foundry.

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Why Azure AI Services Matter for AB-731

The AB-731 exam tests your ability to make strategic decisions about AI implementation—not technical configuration. Exam takers consistently report that differentiating between Azure AI services is one of the biggest challenges:

• "The challenge is remembering which Azure AI service fits what scenario"
• "Study the differences between Vision, Search, and Foundry carefully"
• "I wish I'd spent more time on Azure AI services"

This guide focuses on what each service does and when to recommend it—exactly what you need for the exam.

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Quick Reference: Service Comparison

Service	Primary Purpose	Best For
Azure AI Vision	Image and video analysis	Document processing, object detection, safety monitoring
Azure AI Search	Intelligent search and RAG	Enterprise search, chatbot grounding, knowledge bases
Microsoft Foundry	AI agent development platform	Custom copilots, multi-agent systems, enterprise AI

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Azure AI Vision

What It Does

Azure AI Vision empowers applications to analyse images, read text, and detect faces using prebuilt image tagging, optical character recognition (OCR), and responsible facial recognition—requiring no machine learning expertise.

Core Capabilities

Optical Character Recognition (OCR)

• Extracts printed and handwritten text from images
• Supports 25+ languages including English, French, German, Chinese, Japanese, and Arabic
• Processes photos, invoices, receipts, posters, business cards, and whiteboards
• Fast, synchronous API for near real-time user experiences

Image Analysis 4.0

• Image captioning (auto-generated text descriptions)
• Image tagging (10,000+ concepts and objects)
• Object detection and people detection
• Smart crops for responsive images
• Adult/inappropriate content identification

Spatial Analysis

• Understands people's presence and movements within physical areas
• Real-time monitoring capabilities
• Use cases: retail traffic analysis, workplace safety, queue management

Face Service

• Face detection and recognition
• Touchless access control
• Privacy-focused face blurring

Business Scenarios for the Exam

Scenario	Why Vision is the Answer
A retail company wants to automate product cataloguing from supplier photos	Image Analysis with object detection and tagging
A catering firm needs to extract data from invoices and receipts	OCR for document text extraction
A manufacturing plant requires real-time safety monitoring	Spatial Analysis for people movement detection
An organisation needs to blur faces in security footage for privacy	Face Service with responsible AI compliance

Real-World Success Stories

CATRION automated invoice validation using Azure AI Vision, cutting review time by two-thirds whilst reducing errors.

Goodwill used item detail extraction from photos to streamline listing creation, boosting clothing sales by over 35%.

Dar Engineering combined Azure AI Vision with other services to achieve 3-second response times for data queries and 25% higher accuracy for document processing.

Key Points for AB-731

• Vision is for analysing visual content (images and video)
• Choose Vision when the business need involves extracting information from images
• It's a prebuilt service—no ML expertise required
• Pay-per-transaction pricing model

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Azure AI Search

What It Does

Azure AI Search is a fully managed, cloud-hosted information retrieval platform designed to optimise Retrieval-Augmented Generation (RAG) within generative AI applications. It connects data to AI, enabling agents and large language models to produce reliable, grounded answers.

Core Capabilities

Query Types Supported

• Full-text search (traditional keyword-based)
• Vector search (semantic similarity matching)
• Hybrid search (combines keyword and vector for maximum recall)
• Multimodal queries (text and images)
• Fuzzy search, autocomplete, and geo-spatial search

Two Retrieval Engines

Engine	Status	Best For
Classic Search	Generally Available	Predictable, low-latency queries with single predefined index
Agentic Retrieval	Public Preview	Complex workflows with multi-source access and LLM-assisted planning

RAG (Retrieval-Augmented Generation) Support

Azure AI Search extends LLM capabilities by grounding responses in proprietary content. Key features:

• Vector Search: Matches concepts, not just keywords—essential for RAG
• Hybrid Search: Combines keyword and vector search using Reciprocal Rank Fusion
• Semantic Ranking: Re-scores results based on meaning, not just keywords

When to Choose Azure AI Search vs Database Search

Choose Azure AI Search when:

• Building RAG scenarios with Azure OpenAI
• Requiring hybrid search (vector + full-text)
• Needing semantic ranking capabilities
• Indexing unstructured content
• Advanced search features like reranking are required

Choose database search (e.g., Cosmos DB) when:

• Frequent changes to vectorised fields requiring real-time searchability
• Already using the database for application data
• Minimising data synchronisation complexity

Business Scenarios for the Exam

Scenario	Why Search is the Answer
A law firm needs intelligent search across thousands of legal documents	Hybrid search with semantic ranking for contextual matching
A company wants to ground their chatbot in internal knowledge bases	RAG support with Azure OpenAI integration
An organisation needs enterprise search across SharePoint and OneDrive	Built-in connectors with permission inheritance
A retailer wants semantic product search on their website	Vector search for conceptual matching beyond keywords

Key Points for AB-731

• Search is the "RAG buddy" for Azure OpenAI and ChatGPT experiences
• Choose Search when the business need involves finding information in documents
• Hybrid search provides maximum recall by combining approaches
• Tiered pricing: Free, Basic, Standard (S1-S3), Storage Optimised (L1-L2)

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Microsoft Foundry

What It Does

Microsoft Foundry (formerly Azure AI Foundry/Azure AI Studio) is a unified platform-as-a-service for building, orchestrating, securing, and deploying production-grade AI agents and applications. It combines production-grade infrastructure with developer-friendly interfaces.

Platform Evolution

The platform has evolved significantly:

• Azure AI Studio became Azure AI Foundry
• Azure AI Foundry became Microsoft Foundry
• This represents expanded capabilities, not just a rebrand

Core Capabilities

Foundry Agent Service

Three types of agents:

Agent Type	Description	Best For
Prompt-Based	Declaratively defined with instructions and tools	Simple, single-purpose agents
Workflow	Sequences of actions or orchestrated agents	Complex multi-step processes
Code-First	Containerised agents via frameworks like LangGraph	Maximum customisation

Multi-Model Support

Microsoft Foundry provides access to models from multiple providers:

• Azure OpenAI (GPT models)
• Anthropic Claude
• Meta Llama
• Mistral AI
• DeepSeek
• Cohere and more

Tool Catalogue

Over 1,000 curated Microsoft and partner tools connect agents to:

• Real-time data
• Business systems
• Productivity applications

This transforms agents from simple responders into active problem-solvers.

Open Standards Support

• Model Context Protocol (MCP): Enables agents to call MCP-compatible tools directly
• Agent2Agent (A2A): Enables agent collaboration across different runtimes
• OpenAPI: Standard support for broad tool integration

Enterprise Integration

One-click publishing deploys agents directly to:

• Microsoft 365 Copilot
• Microsoft Teams Chat

No manual setup or manifest editing required.

Business Scenarios for the Exam

Scenario	Why Foundry is the Answer
An enterprise wants to build custom AI agents with multiple LLM providers	Multi-model support with unified API
A company needs to deploy agents to Microsoft Teams	Native integration with one-click publishing
An organisation wants to orchestrate multiple AI agents working together	Agent orchestration with MCP and A2A protocols
A development team needs access to 1,000+ business tools for their agents	Foundry Tool Catalogue

Microsoft Foundry vs Azure Machine Learning

Aspect	Microsoft Foundry	Azure Machine Learning
Focus	Generative AI, agents, copilots	Custom model training, traditional ML
Speed	"Go-fast button" - rapid development	"Go-deep toolkit" - full control
Users	AI application developers	Data scientists
Approach	Low-code/no-code options	Visual workspace + custom code

Choose Foundry when: Building copilots, chatbots, or generative AI features

Choose Azure ML when: Training custom models on proprietary data with traditional ML

Key Points for AB-731

• Foundry is the unified platform for enterprise AI agents
• Choose Foundry when the business need involves building AI applications or agents
• Platform is free to explore—pay only for deployed models and resources
• Supports multi-model strategies (not locked to one provider)

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Decision Framework: Which Service for Which Scenario

Quick Decision Tree

Is the business need about analysing images or video?

• Yes → Azure AI Vision

Is the business need about searching documents or grounding AI in data?

• Yes → Azure AI Search

Is the business need about building AI agents or applications?

• Yes → Microsoft Foundry

Common Exam Question Patterns

AB-731 exam questions typically present scenarios like:

1. "A company wants to..." followed by a business objective
2. Multiple choice with Azure AI services as options
3. Focus on strategic fit, not technical implementation

Example Question Format:

A UK-based retail company wants to automate product cataloguing by extracting information from product photos uploaded by suppliers. Which Azure AI service should they use?

A) Azure AI Search B) Azure AI Vision C) Microsoft Foundry D) Azure Machine Learning

Answer: B) Azure AI Vision — Image Analysis with object detection and tagging is designed for extracting information from visual content.

Integration Patterns

These services often work together:

Pattern	Services	Use Case
Vision + Search	Extract text (OCR) → Index for search	Make scanned documents searchable
Search + Foundry	Agent queries knowledge base	Grounded agent responses with citations
Vision + Foundry	Agent analyses images in workflow	Multimodal agent capabilities
All Three	Vision → Search ← Foundry	Comprehensive document intelligence

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UK Market Considerations

Data Residency

Azure provides UK-based data residency through:

• UK South (London)
• UK West (Cardiff/Durham)

All three services support UK regional deployment, ensuring data remains within UK boundaries.

GPU Compute Availability

Microsoft announced GPU compute capacity for Azure UK South in Q2 2025, enabling UK-resident AI model training and inference—crucial as AI regulations evolve.

Compliance Framework

UK organisations must consider:

• UK GDPR (post-Brexit adaptation)
• Data Protection Act 2018
• Data (Use and Access) Act 2025

Important: UK region deployment addresses data residency but does not automatically ensure compliance. Organisations must implement appropriate governance measures.

ICO Guidance

The Information Commissioner's Office (ICO) is developing AI-specific guidance expected in 2026. UK organisations should monitor announcements and prepare for potential new requirements.

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Licensing Models Summary

Service	Model	Structure
Azure AI Vision	Pay-as-you-go	Per transaction (each feature counted separately)
Azure AI Search	Tiered	Free, Basic, Standard, Storage Optimised
Microsoft Foundry	Platform free	Pay for deployed models and Azure resources

For AB-731, understand the general models rather than specific pricing amounts.

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Exam Tips for Azure AI Questions

What the Exam Tests

• Strategic understanding of when to use which service
• Business scenario matching to appropriate services
• Integration capabilities between services
• General licensing models (not specific prices)
• Responsible AI considerations for each service

What the Exam Does NOT Test

• Specific API endpoints or code syntax
• Detailed configuration steps
• Low-level technical implementation
• Specific pricing amounts

Elimination Strategies

1. Vision = Visual content — If the scenario involves images or video, Vision is likely correct
2. Search = Finding information — If the scenario involves searching or RAG, Search is likely correct
3. Foundry = Building agents — If the scenario involves creating AI applications, Foundry is likely correct

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Frequently Asked Questions

Which Azure AI service is for image analysis?

Azure AI Vision handles all image and video analysis tasks, including OCR, object detection, spatial analysis, and face detection.

What is the difference between Azure AI Search and database search?

Azure AI Search is optimised for RAG scenarios with hybrid search, semantic ranking, and AI enrichment. Database search (like Cosmos DB) is better when you need real-time searchability with frequent data changes.

Is Azure AI Foundry the same as Microsoft Foundry?

Yes—Microsoft Foundry is the current name for what was previously called Azure AI Foundry (and before that, Azure AI Studio). The name change reflects expanded capabilities, but it's the same platform.

Can these services work together?

Absolutely. A common pattern is using Vision to extract text from images, Search to index and retrieve that content, and Foundry to build an agent that queries the knowledge base. These services are designed to complement each other.

Do I need to know Azure pricing for AB-731?

You need to understand general licensing models (pay-as-you-go vs tiered vs platform-free), but not specific pricing amounts.

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Key Takeaways

1. Azure AI Vision is for analysing visual content (images and video)
2. Azure AI Search is the RAG solution for grounding AI in your data
3. Microsoft Foundry is the platform for building AI agents and applications
4. These services complement each other rather than compete
5. Focus on strategic fit for business scenarios, not technical details
6. UK organisations have data residency options through UK South and UK West regions

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Next Steps

Ready to test your knowledge of Azure AI services? Practice with exam-style questions to reinforce these concepts before your AB-731 exam.

Sources:

• Microsoft Learn - AB-731 Study Guide
• Azure AI Vision Documentation
• Azure AI Search Documentation
• Microsoft Foundry Documentation
• Microsoft Tech Community - Foundry Agent Service