AI-102 Practice Question: Implement natural language processing solutions
A healthcare organization is deploying a solution using Azure AI Language to extract medical entities from clinical notes. The solution must comply with HIPAA and support the following requirements: extract medication names, dosages, and frequencies; identify patient conditions; and recognize negated terms (e.g., 'no sign of infection'). Which THREE Azure AI Language features should the organization use?
⚠ Common exam trap
Many exam-takers confuse PII detection with healthcare entity extraction, or assume negation detection is a separate standalone feature rather than a built-in capability of the healthcare NER model.
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
✓
Prebuilt NER for Healthcare
Prebuilt NER for Healthcare is specifically designed to extract medical entities such as medication names, dosages, frequencies, and patient conditions from unstructured clinical text. It is a HIPAA-eligible Azure service that provides domain-specific entity categories, making it the correct choice for the healthcare use case described.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
PII detection
Why it's wrong here
PII detection extracts personal identifiable information, not clinical entities relevant to the requirements.
- ✓
Prebuilt NER for Healthcare
Why this is correct
Prebuilt NER for Healthcare recognizes common clinical entities such as conditions, symptoms, and procedures.
- ✗
Prebuilt NER for Finance
Why it's wrong here
Prebuilt NER for Finance is designed for financial documents, not healthcare.
- ✓
Negation detection
Why this is correct
Negation detection identifies negated phrases like 'no sign of infection' to avoid false positives.
- ✓
Custom Named Entity Recognition (NER)
Why this is correct
Custom NER allows extracting custom entities like medication names, dosages, and frequencies tailored to the domain.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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