Microsoft Certified Azure AI Engineer Associate : AI-102
The Microsoft Certified Azure AI Engineer Associate (AI-102) certification validates your expertise in designing, building, and deploying AI solutions using Microsoft Azure’s Cognitive Services, Azure Machine Learning, and conversational AI tools. This credential is ideal for professionals who implement AI models, build intelligent apps, and work across vision, language, speech, and decision AI capabilities. Earning this certification demonstrates your ability to create responsible, secure, and scalable AI solutions aligned with industry standards.
With Certify360.ai, preparing for the AI-102 exam becomes smarter and more efficient. Our AI-powered platform personalizes your study journey with adaptive learning paths, practical labs, real-time progress analytics, and scenario-based mock tests aligned with Microsoft’s exam structure. Certify360 breaks down complex AI concepts into simple, actionable modules, helping you master hands-on skills—from creating cognitive pipelines to deploying enterprise-grade AI solutions.
Exam Overview
- Number of Questions: 60 questions (multiple choice or multiple response)
- Exam Duration: 85 minutes
- Exam Fee: 99 USD (may vary slightly based on region and currency exchange rates)
- Delivery Options: Online proctored exam, In-person at a Pearson VUE testing center
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Certification study guides for Microsoft Certified Azure AI Engineer Associate
- Architecting Solutions: Design and integrate intelligent applications using Azure AI services, including vision, language, speech, search, and machine learning components.
- Using Core Azure Services: Get hands-on with Azure Cognitive Services, Azure Machine Learning, Bot Framework Composer, and Azure Functions to build scalable AI systems.
- Data Security: Learn to implement secure AI solutions through Azure Key Vault, IAM, RBAC, encrypted endpoints, and responsible AI governance.
- Designing High Availability: Create resilient AI workloads using distributed endpoints, auto-scaling compute, and failover strategies in Azure.
- Cost Optimization: Optimize AI workloads using Azure cost management, pricing calculators, AutoML cost controls, and efficient deployment strategies.
- Monitoring and Troubleshooting: Use Azure Monitor, Application Insights, and diagnostics tools to track AI model accuracy, latency, and operational health.
Best resources for Microsoft Certified Azure AI Engineer Associate
Microsoft Learn – AI-102 Learning Path
Azure Cognitive Services Documentation
Bot Framework & Language Understanding (LUIS) Docs
Responsible AI Standards by Microsoft
Certify360 Mock Exams & Hands-on Labs
GitHub Samples & Azure AI Engineering Community
How to pass Microsoft Certified Azure AI Engineer Associate
Understand the Learning Outcomes
Focus on these core competency areas: planning AI solutions, implementing computer vision, building NLP systems, creating conversational AI, and integrating Azure Machine Learning.
Study how Azure AI services interact across pipelines—covering data ingestion, model deployment, endpoint configuration, and responsible AI guidelines.Hands-On Practice
Build real AI solutions using Azure Cognitive Services, create custom vision models, design bots with Bot Framework, and deploy models with Azure Machine Learning.
Use Azure Sandbox or Certify360 labs to simulate end-to-end pipelines including data preprocessing, model training, and production deployment.Take Practice Exercises
Use Certify360’s practical labs, scenario-based case studies, and AI-102-style mock quizzes to strengthen both conceptual and hands-on expertise.
Practice building and troubleshooting workloads under constraints similar to actual exam scenarios.Review Azure AI Principles & Frameworks
Revisit responsible AI principles such as fairness, reliability, transparency, and inclusiveness.
Explore design patterns for scalable AI architectures—such as distributed inferencing, event-based triggers, microservices, and automated testing pipelines.
Tips to pass Microsoft Certified Azure AI Engineer Associate
a. Understand the Exam Domains
Focus on these areas:
Plan and Manage Azure AI Solutions
Implement Computer Vision Solutions
Implement Natural Language Processing (NLP) Solutions
Implement Knowledge Mining with Azure Cognitive Search
Implement and Integrate Conversational AI Solutions
Use Responsible AI Principles Across the Lifecycle
b. Use Official Resources
- Microsoft Learn paths for AI-102
- Azure AI Services documentation (Vision, Language, Speech, Decision, Cognitive Search)
- Azure OpenAI & AI Studio documentation
- Azure Machine Learning docs for training, deployment, and monitoring
- GitHub samples, SDK reference guides, and Microsoft AI solution accelerators
c. Practice with Real-World Scenarios
Build and deploy end-to-end AI solutions using Azure AI Studio, Custom Vision, Form Recognizer / Document Intelligence, Text Analytics, and Azure Bot Service.
Simulate real project tasks like integrating AI models via REST APIs, securing endpoints with Azure AD, improving accuracy, and optimizing for cost & scalability.
Work on prompt engineering, vision model fine-tuning, knowledge store setup, hybrid search, and multi-service AI pipelines.
d. Take Mock Tests on Certify360
Use domain-based quizzes, hands-on labs, and full exam simulations to master both theory and practical implementation.
Practice scenario-based challenges involving chatbots, document processing, cognitive search, or multimodal AI use-cases.
Review performance analytics to identify weaknesses, reinforce concepts, and refine final exam strategy.
Why you should get the Microsoft Certified Azure AI Engineer Associate ?
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