Combining Entity Recognition and Sentiment Analysis in Azure AI Language
A hospital collects patient experience feedback in free-text form. They need to automatically (1) extract specific mentions of symptoms (e.g., 'headache', 'fever', 'fatigue') from the text, and (2) determine the overall emotional tone of each feedback (e.g., positive, negative, neutral). Which combination of Azure AI Language features should they use?
Quick Answer
The correct answer is entity recognition and sentiment analysis. This combination directly addresses the two distinct requirements: entity recognition extracts specific mentions of symptoms like headache or fever from free-text feedback, while sentiment analysis determines the overall emotional tone—positive, negative, or neutral. In the context of the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how Azure AI Language features map to real-world NLP tasks, often appearing in scenario-based questions where you must pair the right services to solve a problem. A common trap is confusing entity recognition with key phrase extraction—remember, entities are specific named items (symptoms, people, locations), not just important words. For a memory tip, think of it as “who and how”: entity recognition identifies the “who” (or what), and sentiment analysis captures the “how” (the feeling).
⚠ Common exam trap
It's easy for candidates to confuse key phrase extraction with entity recognition, thinking both extract symptoms, but key phrase extraction returns general important phrases without the semantic classification needed for specific symptom identification.
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
✓
A. Entity recognition and sentiment analysis
The hospital needs two distinct NLP capabilities: extracting specific symptom mentions (entity recognition) and determining emotional tone (sentiment analysis). Azure AI Language's entity recognition identifies named entities like symptoms, while sentiment analysis evaluates text for positive, negative, or neutral sentiment. Together, they directly address both requirements without extraneous features.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
A. Entity recognition and sentiment analysis
Why this is correct
Entity recognition extracts named entities such as symptoms from free text, while sentiment analysis returns an overall positive, negative, or neutral tone per document. Together they satisfy both requirements: symptom extraction and emotional tone classification of each patient feedback entry.
- ✗
B. Key phrase extraction and question answering
Why it's wrong here
Key phrase extraction returns salient terms rather than tagged symptom entities, and question answering retrieves answers from a knowledge base instead of scoring sentiment. It is tempting because both are Azure AI Language features; it would be correct for surfacing key topics and answering FAQs from a document set.
- ✗
C. Language detection and text classification
Why it's wrong here
Language detection identifies which language text is written in, and text classification assigns custom or predefined categories, neither extracting symptom entities nor scoring sentiment polarity. It is tempting as a text-analytics pairing; it would be correct for routing multilingual feedback or tagging documents into custom categories.
- ✗
D. Summarization and translation
Why it's wrong here
Summarization condenses text into key sentences and translation converts between languages, so neither extracts symptom mentions nor determines positive, negative or neutral tone. It is tempting because both process free text; it would be correct for producing concise summaries or localising feedback into another language.
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Related to this question
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Document Layout Analysis
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
Key term
Sentiment analysis
Sentiment analysis is a natural language processing technique that uses machine learning to determine the emotional tone or opinion expressed in a piece of text.
About these practice questions
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Same concept, more angles
1 more way this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A hospital collects patient feedback forms in text format. They want to automatically identify whether each feedback is positive, negative, or neutral, and also extract specific recurring phrases like 'waiting time' and 'staff attitude'. Which Azure AI Language feature should they use to determine the overall tone of the feedback?
medium- A.A) Key phrase extraction
- B.B) Entity recognition
- ✓ C.C) Sentiment analysis
- D.D) Language detection
Why C: Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to determine the overall tone (positive, negative, or neutral) of text. The question asks for identifying the tone of feedback, which is exactly what sentiment analysis provides by scoring each document and its sentences for sentiment polarity.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 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-900 exam.