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AI-102 Practice Question: Custom entity extraction in Azure AI Document…
You are building a solution to extract customer feedback from PDF documents stored in Azure Blob Storage. The solution must extract key phrases and sentiment scores, but you cannot use any pre-built models from Azure AI Language. What should you use?
⚠ Common exam trap
A common pitfall is to assume that Azure AI Document Intelligence custom models can handle semantic tasks like key phrase extraction or sentiment analysis. Document Intelligence is designed for structured field extraction (e.g., forms), not natural language understanding. When pre-built Language models are prohibited, custom models in Azure AI Language are the correct choice.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Train custom models in Azure AI Language for key phrase extraction and sentiment analysis
Azure AI Language supports training custom models for named entity recognition (NER) to extract key phrases and custom text classification to predict sentiment labels. You can first extract text from PDFs using Azure AI Document Intelligence or other OCR services, then pass the text to the custom Language models. This approach meets the requirement without using any pre-built Azure AI Language models. Options A and C rely on pre-built APIs and are disallowed. Option D only provides OCR and cannot perform semantic analysis.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Use the sentiment analysis capability in Azure AI Language
Why it's wrong here
Sentiment analysis in Azure AI Language is a pre-built model and is explicitly prohibited by the requirements.
- ✓
Train custom models in Azure AI Language for key phrase extraction and sentiment analysis
Why this is correct
Custom NER and custom text classification in Azure AI Language are not pre-built; you define the entities and labels, making them a valid solution for key phrase extraction and sentiment analysis.
- ✗
Use Azure AI Language's pre-built key phrase extraction API
Why it's wrong here
Pre-built key phrase extraction in Azure AI Language is prohibited.
- ✗
Use Azure AI Document Intelligence with the pre-built read model
Why it's wrong here
The pre-built read model only performs optical character recognition (OCR); it cannot extract key phrases or sentiment scores.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-102 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-102 exam.