AI-102 Exam Preparation (Part 2): Exam Guide
| Select AI-102 Exam Preparation (Part 1): Prerequisities | AI-102 Exam Preparation (Part 1): Prerequisities |
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Here is a 1-week focused study plan for the AI-102: Designing and Implementing a Microsoft Azure AI Solution certification. This is an intensive full-time schedule (6–8 hours/day) aimed at someone who already has a basic understanding of Azure and Python.
🧠 What AI-102 Covers
| Category | Weight |
|---|---|
| Plan and manage an Azure AI solution | 15–20% |
| Implement image and video processing solutions | 20–25% |
| Implement natural language processing (NLP) solutions | 20–25% |
| Implement knowledge mining solutions | 15–20% |
| Implement conversational AI solutions | 15–20% |
✅ Prerequisites You Should Already Have:
- AZ-900 & AI-900-level understanding of Azure + AI
- Python programming experience
- Basics of Azure Cognitive Services, REST APIs, and SDKs
🗓 1-Week AI-102 Study Plan
Goal: Focus on theory, hands-on labs, and practice exams every day.
🔵 Day 1 – Overview + Planning AI Solutions
🔹 Topics:
- AI-102 exam objectives + structure
- Azure AI Solution architecture
- Choosing the right Cognitive Service
- Security (authentication with keys & Azure AD)
- Monitoring and versioning models
🔹 Resources:
- Microsoft Learn:
- MS Docs: Cognitive Services Authentication
- 🧪 Hands-on:
- Deploy a sample Azure AI solution using the portal
- Create a key vault and secure AI credentials
🟣 Day 2 – Computer Vision + Video Indexer
🔹 Topics:
- Computer Vision API, Face API, and Azure Video Indexer
- Read, analyze, and tag images
- Detect objects, people, and text (OCR)
- Spatial analysis, custom vision
🔹 Resources:
- Microsoft Learn:
- 🧪 Hands-on Labs:
- Use SDK to extract text from image
- Build and train a custom vision model
🟢 Day 3 – NLP (Text Analytics, Translator, Language)
🔹 Topics:
- Text Analytics (Sentiment, Entities, Key Phrases, PII)
- Translator (real-time + document translation)
- Language service (QnA Maker successor)
🔹 Resources:
- Microsoft Learn:
- 🧪 Hands-on:
- Sentiment analysis on product reviews
- Create a translation script
- Create and deploy a Language model
🟡 Day 4 – Knowledge Mining (Azure Search + Form Recognizer)
🔹 Topics:
- Azure AI Search (indexers, skillsets, cognitive skills)
- Form Recognizer
- Unstructured data enrichment pipelines
🔹 Resources:
- Microsoft Learn:
- 🧪 Labs:
- Upload PDFs and extract text using Form Recognizer
- Build a searchable knowledge base with Azure AI Search
🔴 Day 5 – Conversational AI (Azure Bot Service + LUIS)
🔹 Topics:
- Bot Framework SDK vs Composer
- Azure Bot Channels
- Recognizers (LUIS vs. CLU)
- QnA, orchestration, dialogs
🔹 Resources:
- Microsoft Learn:
- 🧪 Labs:
- Build a bot with Composer
- Add QnA and Language Understanding to the bot
🟤 Day 6 – Review + Practice Exams
🔹 Morning
- Review all notebooks and key code snippets
- Revisit weak areas (NLP or Form Recognizer, usually)
🔹 Afternoon
- Microsoft Learn: Knowledge Checks on all modules
- Take a full-length practice test
- Example: MeasureUp or Whizlabs
- Review incorrect answers and refresh theory
⚫ Day 7 – Final Practice + Strategy
🔹 Morning
- Flashcards / Rapid-fire review of services, SDKs, and use cases
- Use mind maps to summarize workflows
🔹 Afternoon
- Take another timed full-length mock test
- Deep dive on missed topics
🔹 Evening
- REST API vs SDK review
- Tips for exam day (REST URLs, pricing tiers, keys vs tokens)
🧰 Optional Tools
- Azure Portal (create resources)
- VS Code with Azure extensions
- Python SDK:
azure-cognitiveservices-vision,azure-ai-textanalytics, etc. - Postman (for REST API testing)