AI-103 Text Analysis Practice Question
You are processing medical intake forms using Azure AI Language. You need to identify and categorize medical terms, medications, and dosages. Which feature should you use?
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
✓
Health text analytics
The health text analytics feature (part of Azure AI Language) extracts medical entities and relations specifically tailored for healthcare domains.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Health text analytics
Why this is correct
Health text analytics is designed to extract and label medical-specific entities and assertions.
- ✗
Key phrase extraction
Why it's wrong here
Key phrases do not provide standardized medical ontology categories.
- ✗
Standard Named Entity Recognition (NER)
Why it's wrong here
Standard NER is general-purpose and lacks specialized medical entity categories and relation mapping.
- ✗
Custom text classification
Why it's wrong here
Custom classification categorizes entire documents rather than extracting specific health entities.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed August 2026 · checked against the official Microsoft exam blueprint
This AI-103 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-103 exam.