> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Certification Details
> Concise guide to AI-102 Azure AI Engineer certification, exam domains, required skills, and course resources with labs, mock exams, and study guidance to prepare for the test
Welcome to the AI-102 Certification Details module — your practical roadmap for understanding the exam and how this course prepares you to pass. This lesson outlines the exam structure, highlights the main skill domains you need to master, and shows how to prepare using course resources, mock exams, and official Microsoft materials.
The AI-102 exam evaluates your ability to design and implement AI-powered applications using Microsoft Azure AI Services. Below is a concise, SEO-friendly breakdown of the exam domains and the real-world skills each domain maps to.
High-level overview (by percent of exam)
| Exam Domain | Percentage | Focus / Real-world outcome |
| --------------------------------------------- | ---------: | ----------------------------------------------------------------------------------------- |
| Plan and manage AI solutions | 20–25% | Architecture, governance, cost, and operational planning for Azure AI solutions |
| Develop computer vision solutions | 10–15% | Image and video analysis, face detection, and custom object-detection models |
| Develop natural language processing solutions | 15–20% | Text analytics, question answering, conversational AI, and speech |
| Develop generative AI solutions | 15–20% | Azure OpenAI, prompt engineering, RAG, and integrating generative models |
| Implement knowledge mining solutions | 15–20% | Azure Cognitive Search, Document Intelligence, enrichment pipelines, and knowledge stores |
Below we expand each area with focused takeaways so you know what to study and which hands‑on skills to practice.
Plan and manage AI solutions (20–25%)
* Understand core AI concepts and how Azure AI Services map to solution requirements.
* Choose the right Azure AI components (Vision, Language, Speech, OpenAI, Cognitive Search) and integrate them with enterprise-grade security, monitoring, and cost controls.
* Apply governance and lifecycle practices: authentication (managed identities), role-based access, logging, and responsible AI considerations.
Develop computer vision solutions (10–15%)
* Use prebuilt vision APIs for image analysis, OCR, and face detection.
* Extract searchable metadata from video content and generate frame-level insights.
* Train and evaluate custom models (Azure Custom Vision) to detect domain-specific objects and visual patterns.
* Optimize deployment options for latency and cost (edge vs cloud).
Develop natural language processing solutions (15–20%)
* Perform text analytics: sentiment analysis, key-phrase extraction, language detection, and topic modeling.
* Build question-answering systems and retrieval-backed conversational layers.
* Implement translation and multilingual pipelines with high accuracy.
* Create conversational language understanding solutions: intent recognition, entity extraction, and dialog management.
* Use Document Intelligence to extract structure from documents; implement custom classification and NER for domain-specific labeling.
* Work with speech technologies: speech-to-text, translation, and text-to-speech.
Develop generative AI solutions (15–20%)
* Connect to Azure OpenAI Service to integrate large language models (LLMs) into applications.
* Use SDKs and REST APIs to call models for text generation, summarization, and code generation.
* Improve factuality and relevance using retrieval-augmented generation (RAG) to ground outputs in your documents and databases.
* Apply prompt engineering best practices to design prompts that produce consistent, safe, and useful responses.
Implement knowledge mining solutions (15–20%)
* Build search and indexing solutions with Azure Cognitive Search to surface insights from unstructured data.
* Use Document Intelligence (formerly Form Recognizer) to extract structured fields from invoices, forms, receipts, and contracts.
* Extend enrichment pipelines with Custom Skills for specialized processing.
* Persist enriched, structured data into a Knowledge Store for analytics and queryable outputs.
How this course helps you succeed
* Module-level recap questions reinforce core concepts and check practical knowledge.
* Mock exams simulate the certification environment and highlight knowledge gaps to prioritize study.
* Hands-on labs demonstrate integrations with the Azure SDKs (Python and C#), REST APIs, and Azure Portal workflows.
* Direct links to official Microsoft resources let you review exam objectives and practice with vendor-provided materials.
Before scheduling the exam: ensure you have practical experience with at least one programming language (Python or C#). Most labs and real-world tasks require writing code to call Azure SDKs, manage resources, and integrate services.
Where to find official Microsoft resources
* Official exam page: [AI-102 exam details — Microsoft Learn](https://learn.microsoft.com/en-us/certifications/exams/ai-102/)
* Certification learning path: [Azure AI Engineer — Microsoft Learn](https://learn.microsoft.com/en-us/certifications/azure-ai-engineer/)
* Practice assessments and scheduling: check the official exam page for links to practice tests and Pearson VUE scheduling.
To schedule the exam: sign in with your Microsoft account, select a testing provider (for example, Pearson VUE), and choose a date and time that fits your preparation timeline. Consider taking a practice assessment first to benchmark readiness.
Next steps
Now that you have a clear view of the AI-102 exam structure and the practical skills required, we'll begin a deep dive into planning and managing Azure AI solutions — starting with solution architecture, governance controls, and authentication patterns.