Did you know Microsoft doesn’t care how many services you can memorize? They care if you can build something real. That’s why two use cases — show up again and again on the exam.
1. Document Intelligence/Form Recognizer Use Case
Scenario:
You’re working at a company that receives 10,000 scanned PDF invoices per week.
They want to extract fields like customer name, amount, and date — and then store them in a database. You think, “Okay, OCR. Maybe Computer Vision?” Nope, wrong path.
Microsoft wants
- Form Recognizer
- Trained with custom models if fields vary
- Prebuilt models if the format is consistent (like receipts, IDs, etc.)
- Used with Azure Blob Storage integration
- Optionally called via a Logic App or Azure Function
Why people fail here:
Most assume “text extraction” = computer vision. But Form Recognizer is built for structure — not just words. Also, if you don’t mention:
- Labeling tool usage
- Model versioning
- Secure file ingestion
This is one of the exam’s favorite real-world traps.
2. Conversational AI/Bot + LUIS + QnA Maker Use Case
Scenario:
A healthcare company wants a chatbot to help patients schedule appointments, check symptoms, and answer common FAQs. You build a bot using the Bot Framework. But it keeps getting smarter questions. Now what? Microsoft expects:
- Use LUIS (Language Understanding) to extract intents and entities.
- Use QnA Maker (or Azure Cognitive Search with pre-indexed docs) for FAQ-style matching.
- Dispatch service to route questions between LUIS and QnA
Where people crash:
They treat chatbots as “just call an API and respond.” No handling of:
- Entity recognition
- Disambiguation
- Fallback responses
- Confidence scoring
- Routing logic
This use case is a silent killer because it sounds simple — but it’s architecturally layered.
Microsoft Is Testing Your System Thinking, Not Your API Memory
I thought AI-102 was about knowing what each service does. But the real exam is about:
Given a real-world problem, can you choose the right combination of Azure tools — and deploy them in a way that won’t break at scale?
That’s why:
- A wrong combo = fail
- Ignoring automation = fail
- Skipping model lifecycle, security, or cost tradeoffs = fail
What You Should Do to Pass
If you’re still just watching tutorials and copying examples, do this instead:
1. Build Both Use Cases in Azure
- Start a free Azure account.
- Upload sample invoices → run Form Recognizer end-to-end
- Build a QnA + LUIS chatbot that does more than “Hello world.”
2. Look at Microsoft’s Reference Architectures.
What Microsoft wants.
https://learn.microsoft.com/en-us/azure/architecture/
3. Use the Services Together, Not in Isolation
- Can you send OCR data from Form Recognizer to an Azure Function?
- Can you route a chatbot message based on the NLP confidence score?
- Can you monitor and retrain models using Azure ML pipelines?
That’s the level the AI-102 exam silently demands.
You’re Not Failing Because You’re Dumb — You’re Failing Because You’re Guessing the Wrong Use Cases
Don’t be that person who passes the practice test and crashes on the real one. Start with these two use cases:
- Document AI with Form Recognizer
- Conversational AI with LUIS + QnA
Because they’re not just “services” They’re full-stack problems in disguise.
No comments:
Post a Comment