- A
PII detection
Why wrong: PII detection extracts personal identifiable information, not clinical entities relevant to the requirements.
- B
Prebuilt NER for Healthcare
Prebuilt NER for Healthcare recognizes common clinical entities such as conditions, symptoms, and procedures.
- C
Prebuilt NER for Finance
Why wrong: Prebuilt NER for Finance is designed for financial documents, not healthcare.
- D
Negation detection
Negation detection identifies negated phrases like 'no sign of infection' to avoid false positives.
- E
Custom Named Entity Recognition (NER)
Custom NER allows extracting custom entities like medication names, dosages, and frequencies tailored to the domain.
Question 43 of 993
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
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.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Prebuilt NER for Healthcare uses a specialized model trained on large biomedical corpora (e.g., PubMed, clinical trials) and supports over 150 entity types, including MedicationName, Dosage, Frequency, and Diagnosis. Negation detection is a built-in capability within the healthcare NER model that uses contextual cues (e.g., 'no', 'denies', 'absence of') to mark entities as negated, ensuring accurate clinical interpretation. Custom NER allows fine-tuning on organization-specific terms (e.g., proprietary drug names) that may not be in the prebuilt model's vocabulary.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-102 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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