Are you preparing to take the AI-102: Designing and Implementing a Microsoft Azure AI Solution exam and looking for a comprehensive study guide to help you pass? The Azure AI Engineer Associate certification validates your ability to build, manage, and deploy AI solutions on Microsoft Azure, making it one of the most valuable credentials for professionals working with artificial intelligence and machine learning in the cloud. With AI becoming central to enterprise strategy in 2026, this certification positions you at the forefront of one of the fastest-growing technology domains.
In this article, we will cover everything you need to know to prepare for the AI-102 exam, including the exam objectives and skills measured, recommended study resources, hands-on labs to build practical experience, and tips to help you pass on your first attempt. Whether you’re a developer looking to specialize in AI or an experienced cloud professional expanding your skillset, this guide provides a structured path to certification success.
Why I Took the AI-102 Certification
Who Should Take This Exam?
The AI-102 exam is designed for software developers and AI engineers who build, manage, and deploy AI solutions using Azure AI services. According to Microsoft, ideal candidates should have experience with:
- Python or C# programming languages
- REST APIs and SDKs for building AI solutions
- Azure AI services including Vision, Language, Speech, and Search
- Generative AI and Azure OpenAI Service
- Responsible AI principles and implementation
If you’re coming from a development background and have worked with Azure, you’re well-positioned for this certification. Even if you’re newer to AI, the exam is achievable with dedicated study and hands-on practice.
Learning Objectives
- Plan and manage Azure AI solutions
- Implement computer vision, NLP, and conversational AI
- Integrate Azure Machine Learning into applications
- Apply responsible AI principles and monitor performance
Prerequisites
- Basic programming knowledge (Python or C#)
- Familiarity with REST APIs and JSON
- Hands-on experience with Azure services
- Completion of AZ-900 or AZ-104 (recommended)
- Machine learning fundamentals
- Natural language processing (NLP) basics
- Image and video processing workflows
- Bot development lifecycle
Skills Measured (Updated December 2025)
Microsoft updated the AI-102 exam objectives in December 2025 to reflect the latest Azure AI capabilities, including Azure AI Foundry and agentic solutions. The exam covers six main domains:
Domain 1: Plan and Manage an Azure AI Solution (20-25%)
This domain focuses on selecting appropriate AI services, deploying resources, and implementing security and monitoring.
Key Topics:
- Select the appropriate Microsoft Foundry Services for generative AI, computer vision, NLP, speech, and knowledge mining solutions
- Plan for Responsible AI principles
- Create and configure Azure AI resources
- Choose and deploy AI models using appropriate deployment options
- Install and use SDKs and APIs
- Implement CI/CD pipelines for AI solutions
- Monitor Azure AI resources and manage costs
- Manage authentication and protect account keys
- Implement content moderation and content safety
- Configure content filters, blocklists, and prompt shields
Hands-On Practice: Understanding how to provision and configure Azure AI services is fundamental. I recommend starting with the Azure portal to create your first AI resource, then moving to Infrastructure as Code approaches.
Domain 2: Implement Generative AI Solutions (15-20%)
This is one of the most important domains for 2026, covering Azure OpenAI and the new Azure AI Foundry platform.
Key Topics:
- Plan and deploy generative AI solutions with Microsoft Foundry
- Deploy hubs, projects, and generative AI models
- Implement prompt flow solutions
- Implement RAG (Retrieval-Augmented Generation) patterns
- Evaluate models and flows
- Integrate projects with Microsoft Foundry SDK
- Provision Azure OpenAI resources and deploy models
- Submit prompts for code and natural language generation
- Use DALL-E for image generation
- Configure parameters to control generative behavior
- Implement model monitoring and optimize resources
- Apply prompt engineering techniques
- Fine-tune generative models
Hands-On Practice: Generative AI is best learned by doing. Work with Azure OpenAI in the Azure AI Foundry portal to understand prompt engineering, temperature settings, and how to ground models in your data using RAG patterns.
Domain 3: Implement an Agentic Solution (5-10%)
This is a newer domain added to reflect the growing importance of AI agents in enterprise solutions.
Key Topics:
- Understand the role and use cases of agents
- Configure resources for building agents
- Create agents with Microsoft Foundry Agent Service
- Implement complex agents with Microsoft Agent Framework
- Implement multi-agent orchestration
- Test, optimize, and deploy agents
Hands-On Practice: AI agents represent the next evolution of AI applications. Practice building agents that can perform multi-step tasks, call external APIs, and work autonomously within defined guardrails.
Domain 4: Implement Computer Vision Solutions (10-15%)
This domain covers image and video analysis using Azure AI Vision services.
Key Topics:
- Select visual features for image processing requirements
- Detect objects and generate image tags
- Interpret image processing responses
- Extract text from images using OCR
- Convert handwritten text
- Train and deploy custom vision models for classification and object detection
- Use Azure AI Video Indexer for video insights
- Implement spatial analysis for detecting presence and movement
Hands-On Practice: For hands-on experience with computer vision, I recommend the following lab on CloudLearn.io:
Create and Explore Azure AI Vision in Vision Studio
In this lab, you will create an Azure Computer Vision resource and explore Azure AI Vision Studio. You’ll learn how to:
- Create an Azure Computer Vision resource in the Azure portal
- Connect Azure AI Vision Studio to your resource
- Explore dense captions for detailed image analysis
- Demonstrate object detection with confidence scoring
- Experiment with smart cropping features
This lab provides practical experience with the exact services tested on the exam.
Domain 5: Implement Natural Language Processing Solutions (15-20%)
NLP is a core component of the AI-102 exam, covering text analysis, translation, and speech services.
Key Topics:
- Extract key phrases and entities from text
- Determine sentiment of text
- Detect language and personally identifiable information (PII)
- Translate text and documents using Azure Translator
- Implement text-to-speech and speech-to-text with Azure Speech
- Improve synthesis using SSML (Speech Synthesis Markup Language)
- Implement custom speech solutions
- Translate speech-to-speech and speech-to-text
- Create intents, entities, and utterances for language understanding
- Train and deploy language understanding models
- Create question-answering solutions with multi-turn conversations
Hands-On Practice: Natural language processing requires significant hands-on practice. I recommend these labs on CloudLearn.io:
Analyze Text Using Azure AI Language SDK in Python
Build Python applications that leverage the Azure AI Language SDK to perform comprehensive text analysis. You’ll learn how to:
- Extract key phrases and named entities from business communications
- Implement PII detection and redaction for privacy compliance
- Analyze sentiment and opinion mining
Explore Azure AI Speech Capabilities in Azure AI Foundry
Explore the comprehensive capabilities of Azure AI Speech service. You’ll learn how to:
- Experiment with neural voice synthesis
- Work with the voice gallery
- Create custom personal voices
- Implement speech-to-text recognition
Domain 6: Implement Knowledge Mining and Information Extraction Solutions (15-20%)
This domain covers Azure AI Search, Document Intelligence, and content understanding capabilities.
Key Topics:
- Provision Azure AI Search and create indexes
- Define skillsets and create data sources
- Implement custom skills
- Query indexes with syntax, sorting, filtering, and wildcards
- Manage Knowledge Store projections
- Implement semantic and vector store solutions
- Use Document Intelligence prebuilt and custom models
- Train and publish custom document models
- Create composed document intelligence models
- Create OCR pipelines for text extraction
- Summarize, classify, and detect document attributes
- Extract entities, tables, and images from documents
Hands-On Practice: Knowledge mining and document intelligence are critical exam topics. I recommend these labs on CloudLearn.io:
Building a Knowledge Store with Azure AI Search
Build a complete knowledge store solution using Azure AI Search. You’ll learn how to:
- Configure AI enrichment pipelines
- Define knowledge store projections
- Persist enriched data for downstream analysis
Analyze Forms and Documents with Azure AI Document Intelligence
Learn to provision Azure AI Document Intelligence and analyze documents using prebuilt models. You’ll:
- Create a Document Intelligence resource
- Use Document Intelligence Studio to analyze invoices
- Extract and interpret data from real-world documents
Study Resources
To prepare effectively for the AI-102 exam, I recommend using a combination of official Microsoft resources, hands-on labs, and practice tests.
Official Microsoft Resources
Microsoft Learn Learning Path: The official AI-102 learning path on Microsoft Learn includes interactive modules with built-in sandbox environments. This should be your primary study resource.
Instructor-Led Training: Microsoft offers the AI-102T00-A course “Develop AI solutions in Azure” as a 5-day instructor-led training. This is ideal if you prefer structured classroom learning.
Documentation: Familiarize yourself with the official documentation for each Azure AI service:
- Azure AI Services Overview
- Azure AI Vision
- Azure AI Language
- Azure AI Speech
- Azure AI Search
- Azure OpenAI
- Azure AI Document Intelligence
Hands-On Labs
Theory alone won’t prepare you for this exam. You need hands-on experience building and deploying AI solutions.
CloudLearn Labs: CloudLearn.io offers interactive hands-on labs specifically designed for Azure AI certification preparation. These labs provide real Azure environments where you can practice without worrying about costs or cleanup. Key Azure AI labs include:
| Lab | Exam Domain |
|---|---|
| Create and Explore Azure AI Vision in Vision Studio | Computer Vision (10-15%) |
| Analyze Text Using Azure AI Language SDK in Python | NLP (15-20%) |
| Explore Azure AI Speech Capabilities in Azure AI Foundry | NLP (15-20%) |
| Building a Knowledge Store with Azure AI Search | Knowledge Mining (15-20%) |
| Analyze Forms and Documents with Azure AI Document Intelligence | Knowledge Mining (15-20%) |
I highly recommend completing these labs as part of your exam preparation. They provide the practical experience that Microsoft tests for in the exam.
Practice Tests
Before taking the actual exam, test your knowledge with practice exams:
- Microsoft Learn practice assessments (free)
- MeasureUp official practice tests
- Whizlabs AI-102 practice tests
Study Plan: 6-Week Roadmap
Here’s a structured study plan to prepare for the AI-102 exam:
Week 1-2: Foundation and Planning (Domains 1-2)
Focus Areas:
- Complete Microsoft Learn modules for Azure AI fundamentals
- Understand Azure AI service selection criteria
- Learn Azure OpenAI basics and prompt engineering
- Practice deploying AI resources in Azure portal
Labs to Complete:
- Create your first Azure OpenAI resource
- Experiment with prompts in Azure AI Foundry
Week 3: Generative AI and Agents (Domains 2-3)
Focus Areas:
- Deep dive into RAG patterns
- Learn prompt flow implementation
- Understand agent architectures
- Practice with Azure AI Foundry
Labs to Complete:
- Build a RAG solution with your own data
- Create a simple agent with Foundry Agent Service
Week 4: Computer Vision and NLP (Domains 4-5)
Focus Areas:
- Master image analysis and OCR
- Understand custom vision models
- Learn text analytics and translation
- Practice speech services
Labs to Complete:
- Azure AI Vision in Vision Studio
- Analyze Text Using Azure AI Language SDK
- Azure AI Speech Capabilities
Week 5: Knowledge Mining (Domain 6)
Focus Areas:
- Master Azure AI Search configuration
- Understand skillsets and enrichment pipelines
- Learn Document Intelligence models
- Practice building search solutions
Labs to Complete:
Week 6: Review and Practice Tests
Focus Areas:
- Complete practice assessments
- Review weak areas identified in practice tests
- Revisit hands-on labs for domains with lower scores
- Rest before exam day
Tips for Exam Success
Based on my experience and feedback from others who have passed the AI-102, here are key tips:
1. Focus on Hands-On Experience
The AI-102 exam includes practical scenarios and may include hands-on labs. You cannot pass this exam with theory alone. Spend significant time working in the Azure portal and writing code that calls Azure AI services.
2. Master Python or C# SDKs
You’ll encounter questions about implementing AI solutions using SDKs. Be comfortable with:
# Example: Using Azure AI Language SDK
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
# Authenticate
credential = AzureKeyCredential(key)
client = TextAnalyticsClient(endpoint=endpoint, credential=credential)
# Analyze sentiment
documents = ["I love Azure AI services!"]
response = client.analyze_sentiment(documents)
for doc in response:
print(f"Sentiment: {doc.sentiment}")
3. Understand Responsible AI
Microsoft places significant emphasis on Responsible AI principles. Know how to:
- Implement content moderation
- Configure content filters and blocklists
- Apply prompt shields for safety
- Design governance frameworks
4. Know Service Selection Criteria
Many questions test your ability to select the right Azure AI service for a given scenario:
- Azure AI Vision: Image analysis, OCR, face detection
- Azure AI Language: Sentiment analysis, NER, translation, CLU
- Azure AI Speech: STT, TTS, translation, speaker recognition
- Azure AI Search: Full-text search, semantic search, knowledge mining
- Azure OpenAI: Generative AI, chat, embeddings, image generation
- Document Intelligence: Form extraction, invoice processing, custom models
5. Practice Time Management
The exam has approximately 40-60 questions in 120 minutes. Some questions are straightforward, while case studies take longer. Practice pacing yourself with timed practice tests.
6. Read Questions Carefully
Microsoft exams often include words like “BEST,” “LEAST,” or “MOST” that change the answer. Read each question twice before selecting your answer.
Final Thoughts
The AI-102 Azure AI Engineer Associate certification is an excellent credential for developers and engineers building AI solutions on Microsoft Azure. With the December 2025 exam update incorporating Azure AI Foundry, generative AI, and agentic solutions, this certification validates cutting-edge skills that are in high demand.
Success on this exam requires a combination of theoretical knowledge and practical experience. Use the Microsoft Learn paths for foundational understanding, supplement with hands-on labs from CloudLearn.io to build real-world skills, and test yourself with practice exams before scheduling your test.
The AI landscape is evolving rapidly, and this certification demonstrates your ability to build production-ready AI solutions using the latest Azure services. Good luck with your exam preparation!
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