- A
A. Entity recognition and sentiment analysis
Entity recognition can extract symptom terms as entity types (e.g., medical condition). Sentiment analysis evaluates the overall tone. Together they fulfill both requirements.
- B
B. Key phrase extraction and question answering
Why wrong: Key phrase extraction returns important phrases but does not reliably extract all symptom mentions in a structured way. Question answering is not designed for tone detection.
- C
C. Language detection and text classification
Why wrong: Language detection identifies the language of the text. Text classification could categorize feedback by topic but does not extract symptom mentions.
- D
D. Summarization and translation
Why wrong: Summarization produces a condensed version of the text; it does not extract structured entities. Translation converts text to another language; it does not analyze tone.
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).
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 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?
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
Option A is correct because 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.
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.
- ✓
A. Entity recognition and sentiment analysis
Why this is correct
Entity recognition can extract symptom terms as entity types (e.g., medical condition). Sentiment analysis evaluates the overall tone. Together they fulfill both requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
B. Key phrase extraction and question answering
Why it's wrong here
Key phrase extraction returns important phrases but does not reliably extract all symptom mentions in a structured way. Question answering is not designed for tone detection.
- ✗
C. Language detection and text classification
Why it's wrong here
Language detection identifies the language of the text. Text classification could categorize feedback by topic but does not extract symptom mentions.
- ✗
D. Summarization and translation
Why it's wrong here
Summarization produces a condensed version of the text; it does not extract structured entities. Translation converts text to another language; it does not analyze tone.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Trap categories for this question
Keyword trap
Key phrase extraction returns important phrases but does not reliably extract all symptom mentions in a structured way. Question answering is not designed for tone detection.
Detailed technical explanation
How to think about this question
Entity recognition in Azure AI Language uses a pre-trained model to identify named entities such as medical conditions, symptoms, and medications from unstructured text, leveraging a knowledge base like the Unified Medical Language System (UMLS). Sentiment analysis employs a machine learning classifier that scores each document on a scale from 0 (negative) to 1 (positive), with neutral scores near 0.5, and can also provide sentence-level granularity. In practice, combining these features allows a hospital to automatically flag negative feedback mentioning specific symptoms for urgent review, improving patient care response times.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: A. Entity recognition and sentiment analysis — Option A is correct because 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.
What should I do if I get this AI-900 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.
About these practice questions
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 →
Same concept, more angles
1 more ways 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.
Last reviewed: Jun 11, 2026
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.
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