AI-103 Text Analysis Practice Question
You are processing health-related clinical notes using the Azure AI Language service. You need to extract medical concepts and link them to standardized medical vocabularies such as ICD-10-CM and SNOMED-CT. Which specific feature and endpoint task 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
✓
Healthcare text analytics (Healthcare-specific NER and relation extraction)
Healthcare text analytics is a specialized capability of the Azure AI Language service designed to extract and link medical entities to standard terminologies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Named Entity Recognition (NER) with standard prebuilt-entity models
Why it's wrong here
Standard NER extracts general entities like people, locations, and organizations, not specialized medical codes like ICD-10.
- ✗
Key phrase extraction with medical domain parameters
Why it's wrong here
Key phrase extraction does not link terms to standardized clinical terminologies or vocabularies.
- ✗
Custom Named Entity Recognition with a trained ICD-10 dictionary
Why it's wrong here
While Custom NER can be trained, healthcare text analytics provides a prebuilt, production-ready specialized clinical model.
- ✓
Healthcare text analytics (Healthcare-specific NER and relation extraction)
Why this is correct
Healthcare text analytics extracts health-related entities and links them to specialized ontologies like UMLS, SNOMED-CT, and ICD-10.
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.