AI-103 Certification Study Guide: Every Exam Objective Mapped, Domain Weights, 6-Week Study Plan, Microsoft Foundry, Generative AI, Agents, RAG, Computer Vision, Text Analysis, Document Intelligence, and How to Pass

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This is the master study guide for the AI-103: Developing AI Apps and Agents on Azure exam — Microsoft’s Associate-level certification for building AI applications and intelligent agents on Azure. AI-103 replaced the retired AI-102 in June 2026 and shifts the focus heavily toward Microsoft Foundry, generative AI, RAG pipelines, and agentic solutions — the tools and patterns that production AI teams use in 2026.

This post maps every exam objective to our blog posts, gives you a 6-week study plan weighted by domain importance, and tells you exactly what to expect on exam day.

Analogy — A building inspection certification. Imagine you are getting certified as a building inspector. You need to know the building codes (Azure AI services), how to inspect foundations (plan and manage), how to evaluate electrical systems (generative AI and agents), plumbing (computer vision), HVAC (text analysis), and fire safety (information extraction). The exam does not ask you to memorize every code number — it gives you a scenario (“This building has these characteristics — which code applies?”) and tests whether you can choose the right inspection approach. AI-103 works the same way: scenario-based questions that test whether you can assemble the right Azure AI services for a given requirement.

What Is AI-103 and Who Is It For

Official name: AI-103: Developing AI Apps and Agents on Azure
Certification: Microsoft Certified: Azure AI Apps and Agents Developer Associate
Replaces: AI-102 (retired June 30, 2026)
Status: Generally Available (launched April 2026 as beta, now GA)

Who should take this exam:
  - Azure AI engineers who build and deploy AI applications
  - Data engineers expanding into AI/ML
  - Backend developers building AI-powered features
  - Anyone who works with Azure OpenAI, Microsoft Foundry, or Azure AI services

Prerequisites (recommended, not required):
  - Python development experience
  - Familiarity with Azure (subscriptions, resource groups, RBAC)
  - Basic understanding of AI/ML concepts (our AI/ML series covers this)
  - Experience with REST APIs and SDKs

What it validates:
  - Planning and managing Azure AI solutions (Foundry, networking, security)
  - Building generative AI applications (prompt engineering, RAG, grounding)
  - Building AI agents (Responses API, function calling, memory, tools)
  - Implementing computer vision (image analysis, OCR, object detection)
  - Implementing text analysis (NLP, sentiment, translation, speech)
  - Implementing information extraction (Document Intelligence, AI Search)

Exam Format and Logistics

Duration: 120 minutes
Questions: 40-60 (varies per exam session)
Passing score: 700 out of 1000
Cost: $165 USD (standard pricing)
Language: English only (as of 2026)
Delivery: Pearson VUE (online proctored or test center)

Question types:
  - Multiple choice (single answer)
  - Multiple select (choose 2 or more)
  - Drag-and-drop (order steps, match services)
  - Case studies (read a scenario, answer 4-5 related questions)
  - Possibly interactive lab tasks (configure in a simulated portal)

Scoring:
  - Each question has equal or near-equal weight
  - No penalty for wrong answers (always answer every question)
  - Case study questions may have partial credit
  - Score is scaled -- raw percentage does not equal final score

What you CAN do during the exam:
  - Use the built-in Microsoft Learn reference (some exams allow this)
  - Flag questions for review and return to them
  - Take breaks between sections (if the exam has sections)

What you CANNOT do:
  - Use external resources, notes, or second monitors
  - Copy questions or take screenshots
  - Use AI assistants

AI-102 vs AI-103 — What Changed

AI-102 (retired June 30, 2026):
  - Focused on individual Azure AI services (Cognitive Services)
  - Heavy on: LUIS, QnA Maker, Custom Vision, Form Recognizer
  - Light on: generative AI, agents, RAG
  - No Microsoft Foundry (it didn't exist yet)
  - Essentially a "which API do I call?" exam

AI-103 (current):
  - Focused on Microsoft Foundry as the unified platform
  - Heavy on: generative AI (30-35%), RAG, agents, prompt engineering
  - Foundry replaces individual service portals
  - LUIS → replaced by CLU (Conversational Language Understanding)
  - QnA Maker → replaced by RAG with Azure AI Search
  - Custom Vision → still tested but through Foundry lens
  - Form Recognizer → renamed to Document Intelligence
  - New: Responses API for agents, Content Understanding, evaluation metrics
  - New: Content Safety as a first-class domain

The shift:
  AI-102: "Here is a document. Which Azure service extracts entities from it?"
  AI-103: "Here is a business requirement. Design a Foundry solution with
           RAG, an agent, content safety, and proper monitoring."

If you studied for AI-102:
  - Pivot to Foundry immediately (it is the platform for everything)
  - Add RAG and agent patterns (they did not exist in AI-102)
  - Update service names (Form Recognizer → Document Intelligence)
  - Keep your computer vision and NLP knowledge (still tested, 10-15% each)

The Five Exam Domains at a Glance

DomainWeightFocusKey Services
1. Plan and Manage25-30%Platform setup, security, governanceFoundry Hubs/Projects, Entra ID, Private Endpoints, Content Safety, Application Insights
2. Generative AI and Agents30-35%The biggest domainAzure OpenAI, Foundry agents, Responses API, RAG, AI Search, Prompt Flow, Evaluation
3. Computer Vision10-15%Image and video analysisAzure AI Vision, Image Analysis 4.0, Custom Vision, Face API, Content Understanding
4. Text Analysis10-15%NLP, speech, translationAzure AI Language, CLU, Sentiment, NER, PII, Speech, Translator
5. Information Extraction10-15%Documents and searchDocument Intelligence, AI Search, Skillsets, Vector Search, Content Understanding

Study time allocation (follow the weights):

  Domain 2 (30-35%): 35% of your study time → ~2.5 weeks
  Domain 1 (25-30%): 25% of your study time → ~1.5 weeks
  Domain 3 (10-15%): 13% of your study time → ~1 week
  Domain 4 (10-15%): 13% of your study time → ~1 week
  Domain 5 (10-15%): 14% of your study time → ~1 week

  Domains 1 and 2 together = 55-65% of the exam.
  If you ace those two, you only need moderate scores on 3, 4, and 5 to pass.

Domain 1 — Plan and Manage an Azure AI Solution (25-30%)

Analogy — The architect’s blueprint and building permits. Before you build anything, you need a plan: where will it go (region selection), who can enter (identity and access), how is it secured (networking), does it meet regulations (Responsible AI), and how do you know if something breaks (monitoring). Domain 1 is the planning and governance layer.

Sub-objectives:

  1.1 Select appropriate Azure AI services
    - Choose between Azure OpenAI, Azure AI services, and custom models
    - Select deployment types: serverless API vs managed compute
    - Understand the Foundry Model Catalog (1,800+ models)
    - Choose the right model for the task (GPT-4o, Phi, Llama, Mistral)

  1.2 Plan and manage a Microsoft Foundry solution
    - Create and configure AI Hubs (shared governance container)
    - Create and configure AI Projects (team/app workspace)
    - Manage model deployments (endpoints, quotas, TPM limits)
    - Configure connections to external data sources
    - Manage compute resources and cost

  1.3 Implement authentication and authorization
    - Microsoft Entra ID (formerly Azure AD) for identity
    - Managed identities (system-assigned and user-assigned)
    - RBAC roles for Foundry resources
    - API key management and rotation
    - Service principal authentication for CI/CD

  1.4 Implement network security
    - Private endpoints for Foundry resources
    - VNet integration
    - Network Security Groups (NSGs)
    - Azure Private Link

  1.5 Monitor Azure AI solutions
    - Application Insights for tracing and telemetry
    - Azure Monitor for resource health
    - Logging model inputs and outputs
    - Cost monitoring and quota management
    - Evaluation metrics (groundedness, relevance, coherence, fluency)

  1.6 Implement Responsible AI
    - Azure AI Content Safety (violence, hate, sexual, self-harm)
    - Content filters with configurable severity thresholds
    - Prompt Shields (jailbreak detection)
    - Groundedness detection (hallucination prevention)
    - Protected material detection (copyright)
    - Custom blocklists
    - Microsoft Responsible AI principles

Domain 2 — Implement Generative AI and Agentic Solutions (30-35%)

Analogy — Building a smart assistant from scratch. You start by teaching it how to talk (prompt engineering). Then you give it a library card so it can look up facts (RAG with Azure AI Search). Then you give it tools — a calculator, a phone, a calendar (function calling). Then you give it a memory so it remembers previous conversations. Then you evaluate whether it is actually helping or making things up. Domain 2 covers this entire journey.

Sub-objectives:

  2.1 Implement prompt engineering
    - System messages (persona, rules, constraints)
    - Few-shot prompting (examples in the prompt)
    - Chain-of-thought prompting (step-by-step reasoning)
    - Temperature and top-p (controlling randomness)
    - Max tokens and stop sequences
    - Output formatting (JSON mode, structured outputs)
    - Prompt templates and parameterization

  2.2 Implement grounding and retrieval
    - RAG (Retrieval Augmented Generation) architecture
    - Azure AI Search as the retrieval layer
    - Vector search (dense embeddings)
    - Semantic search (semantic ranker)
    - Hybrid search (keyword + vector + semantic)
    - Chunking strategies (fixed-size, semantic, sentence-based)
    - Embedding models (text-embedding-3-large, text-embedding-3-small)
    - On Your Data feature in Azure OpenAI

  2.3 Implement AI agents
    - Azure AI Agent Service (Responses API)
    - Agent configuration (instructions, model, tools)
    - Function calling / tool use (defining tool schemas)
    - Code Interpreter tool
    - File Search tool
    - Agent memory (conversation history, thread management)
    - Multi-agent orchestration
    - Handoff between agents

  2.4 Implement Prompt Flow
    - Visual and code-based orchestration
    - Flow types (standard, chat, evaluation)
    - Connecting nodes (LLM, Python, tools)
    - Deploying flows as endpoints
    - Evaluation flows for quality and safety

  2.5 Evaluate generative AI solutions
    - Built-in evaluators: groundedness, relevance, coherence, fluency, similarity
    - Safety evaluators: violence, hate, self-harm, sexual
    - Custom evaluators
    - Running evaluations in Foundry
    - A/B testing model configurations
    - Tracing with Application Insights

Domain 3 — Implement Computer Vision Solutions (10-15%)

Analogy — Teaching a robot to see. First, it learns to describe what it sees (image analysis — captions, tags). Then it learns to find specific objects (object detection — “there is a car at position x, y”). Then it learns to read text in images (OCR). Then it learns to recognize faces. Domain 3 covers the robot’s entire visual education.

Sub-objectives:

  3.1 Analyze images
    - Azure AI Vision Image Analysis 4.0
    - Generate captions and dense captions
    - Extract tags (keywords from images)
    - Smart crop (content-aware thumbnails)
    - People detection
    - Background removal

  3.2 Implement custom vision models
    - Custom Vision classification (is this a cat or a dog?)
    - Custom Vision object detection (where is the cat in the image?)
    - Training iterations and publishing
    - Prediction endpoint

  3.3 Implement OCR (Optical Character Recognition)
    - Read API (extract text from images and documents)
    - Handwriting recognition
    - Multi-language support
    - Document layout analysis

  3.4 Analyze video and spatial data
    - Content Understanding for video analysis
    - Spatial analysis (people counting, zone monitoring)
    - Video retrieval and indexing

  3.5 Implement face detection and analysis
    - Face API (detect, verify, identify, group)
    - Face attributes (age, emotion, glasses, head pose)
    - Access restrictions (Microsoft approval required for identification)
    - Responsible use of facial recognition

Domain 4 — Implement Text Analysis Solutions (10-15%)

Analogy — Teaching a robot to read and listen. The robot learns to understand the mood of a text (sentiment analysis), identify important names and places (entity recognition), detect sensitive information (PII detection), translate between languages, and convert speech to text and text to speech. Domain 4 covers language comprehension.

Sub-objectives:

  4.1 Analyze text
    - Azure AI Language service
    - Sentiment analysis (positive, negative, neutral, mixed)
    - Key phrase extraction
    - Named Entity Recognition (NER) -- person, location, organization, datetime
    - Entity linking (connecting entities to Wikipedia/knowledge base)
    - PII detection and redaction
    - Text summarization (extractive and abstractive)

  4.2 Implement language understanding
    - Conversational Language Understanding (CLU) -- replacement for LUIS
    - Intents and entities
    - Training and publishing CLU models
    - Custom text classification
    - Custom NER (training your own entity types)

  4.3 Implement translation
    - Azure AI Translator
    - Text translation (100+ languages)
    - Document translation (batch)
    - Custom Translator (domain-specific translations)
    - Language detection

  4.4 Implement speech capabilities
    - Speech-to-Text (STT) -- real-time and batch
    - Text-to-Speech (TTS) -- neural voices
    - Speech translation (real-time)
    - Custom Speech models (domain-specific vocabulary)
    - Speaker recognition and verification
    - Pronunciation assessment

Domain 5 — Implement Information Extraction Solutions (10-15%)

Analogy — Teaching a robot to read documents and organize a library. The robot learns to read invoices, receipts, and forms (Document Intelligence). Then it learns to organize documents in a searchable library with a catalog system (Azure AI Search). It can even search by meaning, not just keywords (vector search). Domain 5 covers turning unstructured documents into structured, searchable knowledge.

Sub-objectives:

  5.1 Implement document intelligence
    - Azure AI Document Intelligence (formerly Form Recognizer)
    - Prebuilt models: invoice, receipt, ID document, W-2, health insurance
    - Custom models: template-based and neural
    - Compose models (combine multiple custom models)
    - Layout analysis (tables, key-value pairs, selection marks)

  5.2 Implement Azure AI Search
    - Create and manage search indexes
    - Index documents from Blob Storage, SQL, Cosmos DB
    - Built-in skillsets (OCR, entity recognition, key phrases, language detection)
    - Custom skills (call your own API during indexing)
    - Vector search configuration (embedding dimensions, distance metrics)
    - Semantic ranker (AI-powered relevance)
    - Hybrid search (combine keyword, vector, and semantic)

  5.3 Implement Content Understanding
    - Azure AI Content Understanding (new in AI-103)
    - Analyzer templates with natural-language schemas
    - Extract structured data from documents, images, audio, and video
    - Compare with Document Intelligence (when to use which)

  5.4 Build RAG ingestion pipelines
    - Chunking documents for search (fixed-size, semantic, page-based)
    - Generating embeddings for vector search
    - Indexer schedules and change detection
    - Integrated vectorization (AI Search handles embedding automatically)
    - OCR integration for scanned documents

Every Objective Mapped to Blog Posts

This section will update as we publish the AI-103 series.
Each post covers one or two sub-domains in detail:

  Domain 1: Plan and Manage
    → Post 2: Microsoft Foundry Platform (Hubs, Projects, Models, Deployments)
    → Post 3: Security, Networking, Responsible AI, and Monitoring

  Domain 2: Generative AI and Agents
    → Post 4: Prompt Engineering (system messages, few-shot, CoT, grounding)
    → Post 5: RAG Pipelines (AI Search, vector search, chunking, embeddings)
    → Post 6: AI Agents (Responses API, function calling, memory, evaluation)

  Domain 3: Computer Vision
    → Post 7: Computer Vision (Image Analysis 4.0, Custom Vision, OCR, Face API)

  Domain 4: Text Analysis
    → Post 8: Text Analysis and Speech (Language, CLU, NER, PII, Speech)

  Domain 5: Information Extraction
    → Post 9: Document Intelligence (prebuilt/custom models, AI Search, Content Understanding)

  Background knowledge (already published):
    → AI/ML Introduction (classification, regression, algorithms)
    → Feature Engineering (encoding, scaling, selection)
    → Neural Networks and Deep Learning (backpropagation, transformers)
    → Fine-Tuning LLMs (the foundation for understanding generative AI)
    → Azure Key Vault (secrets management, used in Foundry connections)
    → Azure RBAC (roles, managed identities, service principals)
    → Azure Networking (VNets, NSGs, private endpoints)

The 6-Week Study Plan

Week 1: Platform Foundations (Domain 1, Part 1)
  Day 1-2: Create a Microsoft Foundry resource in Azure portal
           Understand Hubs vs Projects, create both
           Explore the Model Catalog, deploy GPT-4o
  Day 3-4: Authentication deep dive
           Configure managed identity for Foundry
           Set up RBAC roles, test access control
  Day 5:   Networking: configure private endpoints
           Understand VNet integration for Foundry
  Day 6-7: Read Blog Post 2 (Microsoft Foundry Platform)
           Practice: deploy a model, create a project, test the API

Week 2: Security, Governance, and Responsible AI (Domain 1, Part 2)
  Day 8-9: Azure AI Content Safety
           Configure content filters (severity thresholds)
           Test Prompt Shields and groundedness detection
  Day 10:  Monitoring with Application Insights
           Set up tracing for model calls
           Configure alerts for error rates
  Day 11-12: Responsible AI principles
             Microsoft's 6 principles
             Implement content filters in a deployment
  Day 13-14: Read Blog Post 3 (Security, Networking, Responsible AI)
             Practice: 25 Domain 1 questions

Week 3: Generative AI -- Prompts and RAG (Domain 2, Part 1-2)
  Day 15-16: Prompt engineering hands-on
             System messages, few-shot, chain-of-thought
             Temperature, top-p, JSON mode
  Day 17-18: RAG architecture
             Create an Azure AI Search index
             Configure vector search with embeddings
             Implement hybrid search (keyword + vector + semantic)
  Day 19-20: Chunking strategies
             Build a RAG pipeline: Blob → AI Search → OpenAI
             Test grounding with "On Your Data"
  Day 21:    Read Blog Posts 4 and 5 (Prompt Engineering + RAG)
             Practice: 25 Domain 2 questions

Week 4: Agents and Evaluation (Domain 2, Part 3)
  Day 22-23: Build an AI agent with Azure AI Agent Service
             Configure tools (function calling, code interpreter, file search)
             Implement agent memory and thread management
  Day 24-25: Multi-agent orchestration
             Agent handoff patterns
             Evaluation: groundedness, relevance, coherence, fluency
  Day 26:    Prompt Flow: build and deploy a flow
  Day 27-28: Read Blog Post 6 (AI Agents and Evaluation)
             Practice: 25 Domain 2 questions
             CHECKPOINT: Domain 2 is 30-35% — do not advance with weak understanding

Week 5: Vision, Text, Speech, and Extraction (Domains 3, 4, 5)
  Day 29-30: Computer Vision
             Image Analysis 4.0 (captions, tags, OCR)
             Custom Vision (classification, object detection)
             Face API capabilities and restrictions
  Day 31-32: Text Analysis and Speech
             Azure AI Language (sentiment, NER, PII, summarization)
             CLU (intents and entities -- LUIS replacement)
             Speech-to-Text, Text-to-Speech, translation
  Day 33-34: Information Extraction
             Document Intelligence (prebuilt and custom models)
             Azure AI Search indexing pipeline
             Content Understanding analyzers
  Day 35:    Read Blog Posts 7, 8, and 9
             Practice: 15 questions each for Domains 3, 4, 5

Week 6: Review and Practice Exams
  Day 36-37: Full practice exam #1 (timed, 60 questions, 120 minutes)
             Review every wrong answer
             Identify weak domains
  Day 38-39: Targeted review of weak domains
             Re-read blog posts for weak areas
             Rebuild any hands-on labs that were unclear
  Day 40:    Full practice exam #2 (timed)
             Target: 80%+ before scheduling the real exam
  Day 41-42: Light review, rest, schedule the exam
             Skim blog posts one final time
             No new material -- consolidate what you know

Study Resources

Free resources:
  1. Microsoft Learn -- AI-103 learning paths (official, free)
     https://learn.microsoft.com/en-us/credentials/certifications/exams/ai-103/
  2. DriveDataScience AI-103 blog series (this site -- 9 posts)
  3. Microsoft Learn documentation for each Azure AI service
  4. Azure free tier -- $200 credit for hands-on labs

Practice exams:
  1. Microsoft Practice Assessment (free, official -- check availability)
  2. Third-party practice exams (MeasureUp, Whizlabs, MSCertQuiz)
  3. Always review explanations, not just answers

Hands-on labs:
  1. Create a Foundry Hub and Project → deploy GPT-4o
  2. Build a RAG pipeline: Blob Storage → AI Search → Azure OpenAI
  3. Create an AI agent with function calling
  4. Run evaluations (groundedness, relevance, coherence)
  5. Implement Content Safety filters
  6. Build a Custom Vision classifier
  7. Use Document Intelligence on an invoice
  8. Create an AI Search index with skillsets

Exam Day Tips

Before the exam:
  - Schedule for a time when you are sharpest (morning for most people)
  - Ensure stable internet if taking online (wired connection preferred)
  - Close all other applications
  - Have your ID ready (two forms for online proctored)
  - Use the bathroom before starting (120 minutes is a long time)

During the exam:
  - Read every question completely before looking at answers
  - Eliminate obviously wrong answers first (usually 1-2 are clearly wrong)
  - Flag uncertain questions and return to them after finishing
  - Do not spend more than 3 minutes on any single question
  - For case studies: read the scenario overview ONCE, then reference it per question
  - Budget time: 60 questions ÷ 120 minutes = 2 minutes per question
  - Always answer every question (no penalty for wrong answers)

Question patterns to watch for:
  - "LEAST administrative effort" = simplest solution that works
  - "MOST secure" = managed identity > service principal > API key
  - "Minimize cost" = serverless > managed compute for low volume
  - "Real-time" = often points to DirectQuery, streaming, or live endpoints
  - "Ensure compliance" = Content Safety, Content Filters, Responsible AI
  - "Which service?" = match the scenario to the correct Azure AI service

Common Mistakes

  1. Studying AI-102 material for AI-103. AI-102 retired in June 2026. Study guides published before spring 2026 cover LUIS, QnA Maker, and the old Cognitive Services model — services that are deprecated or renamed. AI-103 centers on Microsoft Foundry, the Responses API for agents, RAG with AI Search, and Content Understanding. Use only AI-103-specific materials dated April 2026 or later.

  2. Memorizing service names without understanding when to use each. AI-103 is scenario-based. It does not ask “What is Azure AI Vision?” It asks “A company needs to extract text from scanned invoices in multiple languages. Which service and which deployment pattern should they use?” Understanding the decision criteria (when Document Intelligence vs Content Understanding vs OCR Read API) matters more than remembering feature lists.

  3. Skipping hands-on practice. Reading Microsoft Learn documentation is necessary but not sufficient. AI-103 tests whether you can configure services, not whether you can describe them. Create a Foundry project, deploy a model, build a RAG pipeline, create an agent — the exam asks about configurations, parameters, and error scenarios that you only encounter by building.

  4. Under-studying Domain 2 (Generative AI and Agents). At 30-35%, this is the largest domain. It covers prompt engineering, RAG architecture, AI Search, agents, function calling, memory, multi-agent orchestration, and evaluation. Weak performance here cannot be compensated by perfect scores on the smaller domains.

  5. Ignoring Responsible AI and Content Safety. Content Safety is woven into multiple domains — it appears in Domain 1 (planning) and Domain 2 (implementation). Questions about content filters, Prompt Shields, groundedness detection, and protected material detection are common. Know how to configure severity thresholds and when to use each safety feature.

  6. Not understanding the difference between similar services. Document Intelligence vs Content Understanding. Azure AI Search vs Azure OpenAI “On Your Data.” Custom Vision vs Image Analysis 4.0 custom models. CLU vs the deprecated LUIS. The exam tests these distinctions explicitly. Build a comparison table for overlapping services.

  7. Spending equal time on all five domains. The weights are intentional: Domains 1 and 2 together account for 55-65% of the exam. Spending equal time on all five means under-preparing for the majority of questions. Allocate study time proportionally to domain weights.

  8. Not taking timed practice exams. Knowing the material is different from performing under time pressure. Two minutes per question means you cannot deliberate extensively. Practice with timed mock exams (60 questions, 120 minutes) at least twice before the real exam. Target 80%+ consistently before scheduling.

Interview Questions

Q: What is AI-103 and how does it differ from AI-102? A: AI-103 (Developing AI Apps and Agents on Azure) is the Associate-level certification that replaced AI-102 in June 2026. AI-102 focused on individual Azure Cognitive Services (LUIS, QnA Maker, Custom Vision, Form Recognizer). AI-103 centers on Microsoft Foundry as the unified platform, with the largest domain (30-35%) covering generative AI, RAG pipelines, and AI agents using the Responses API. It reflects the industry shift from calling individual AI APIs to building complete AI solutions with grounding, agents, safety, and evaluation.

Q: What are the five exam domains and their weights? A: Domain 1: Plan and Manage an Azure AI Solution (25-30%) covers Foundry setup, security, networking, monitoring, and Responsible AI. Domain 2: Implement Generative AI and Agentic Solutions (30-35%) covers prompt engineering, RAG, agents, Prompt Flow, and evaluation — the largest domain. Domain 3: Computer Vision (10-15%) covers Image Analysis, Custom Vision, OCR, and Face API. Domain 4: Text Analysis (10-15%) covers Language Understanding, sentiment, NER, PII, speech, and translation. Domain 5: Information Extraction (10-15%) covers Document Intelligence, AI Search, and Content Understanding.

Q: What is Microsoft Foundry and why is it central to AI-103? A: Microsoft Foundry (formerly Azure AI Studio) is the unified platform for the entire AI application lifecycle on Azure. It provides the Model Catalog (1,800+ models), project management (Hubs and Projects), agent configuration (Responses API), RAG orchestration (Prompt Flow), evaluation tools, content safety, and monitoring. AI-103 is essentially a Foundry exam — nearly every domain tests skills within the Foundry ecosystem. Understanding the Foundry platform architecture (Hubs, Projects, deployments, connections) is foundational to all five domains.

Q: What is the recommended study approach for AI-103? A: Follow the domain weights. Spend 60% of study time on Domains 1 and 2 (combined 55-65% of the exam). Build hands-on: create a Foundry project, deploy a model, build a RAG pipeline with Azure AI Search, create an agent with function calling, run evaluations, and configure Content Safety filters. Take at least two timed practice exams (60 questions, 120 minutes) and review every wrong answer. Target 80%+ on practice exams before scheduling the real exam. A 6-week plan with one week per domain and one week for review works well.

Q: What are the key Azure AI services tested in AI-103? A: Microsoft Foundry (the platform for everything), Azure OpenAI (GPT-4o, embeddings), Azure AI Search (RAG retrieval, vector/hybrid/semantic search), Azure AI Agent Service (Responses API for agents), Azure AI Content Safety (content filters, Prompt Shields), Azure AI Vision (Image Analysis 4.0, Custom Vision, Face API), Azure AI Language (sentiment, NER, CLU, PII), Azure AI Speech (STT, TTS, translation), Azure AI Document Intelligence (prebuilt and custom document models), and Azure AI Content Understanding (multimodal extraction).

Q: How should a data engineer approach AI-103 preparation? A: Data engineers already have strong foundations in Azure (RBAC, networking, Key Vault), Python, and data pipeline design. Leverage these for Domain 1 (planning and security). Focus new learning on Foundry platform specifics, prompt engineering patterns, RAG architecture (data engineers understand ETL — RAG ingestion is similar), and agent configuration. The Document Intelligence and AI Search domains connect to data pipeline skills. The main gap is typically prompt engineering and agent patterns, which require hands-on practice in Foundry.

Wrapping Up

AI-103 is a builder’s exam, not a vocabulary test. It tests whether you can assemble production-ready AI solutions using Microsoft Foundry: choose the right model, ground it with search, wrap it in an agent with tools and memory, evaluate it for quality and safety, and secure and monitor it in production. The five domains cover the complete lifecycle from planning to deployment.

The 6-week study plan follows the domain weights: two weeks on Foundry platform management and security, two weeks on generative AI, RAG, and agents (the largest domain), one week on vision, text, speech, and extraction, and one week on review and practice exams. Build real solutions in Foundry — the exam tests configurations and trade-offs you only learn by doing.

The next post in this series dives into Microsoft Foundry — the platform that underpins everything: Hubs, Projects, the Model Catalog, deployment types, connections, and the AI development lifecycle.

Related posts: – AI/ML Introduction – Neural Networks and Deep Learning – Fine-Tuning LLMs – Azure Key Vault – Azure RBAC – Azure Networking

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