Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
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?
⚠ Common exam trap
Candidates often confuse key phrase extraction (which extracts phrases like 'waiting time') with sentiment analysis, but key phrase extraction does not determine tone—it only identifies significant terms without any sentiment scoring.
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
✓
C) Sentiment analysis
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.
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) Key phrase extraction
Why it's wrong here
Key phrase extraction in Azure AI Language identifies salient words and multi-word expressions (e.g., 'long wait time', 'nurse professionalism') that define the main topics in a patient's feedback. It does not, however, assign an emotional label or polarity to the text, so it cannot reveal whether the opinion is positive or negative. This makes it a poor fit for the stated task of determining the overall tone of the feedback.
- ✗
B) Entity recognition
Why it's wrong here
Entity recognition (NER) uses a pretrained model to locate and categorize named entities such as persons, organizations, locations, dates, and medical terms into predefined types. For a patient comment, it might detect 'Emergency Department' as a location or 'Dr. Smith' as a person, but it produces no judgment about the expressed sentiment. Since the hospital needs to gauge satisfaction — not just extract factual references — NER fails to address the core requirement.
- ✓
C) Sentiment analysis
Why this is correct
Sentiment analysis is a text classification capability in Azure AI Language that evaluates each input document or sentence and returns a sentiment label (positive, negative, neutral, or mixed) along with confidence scores. Under the hood, it uses trained machine learning models on contextual embeddings to capture how the overall attitude is expressed. This directly matches the task of identifying the overall tone of patient feedback, making it the correct choice.
- ✗
D) Language detection
Why it's wrong here
Language detection scans the text and, based on character n-grams and word patterns, identifies the dominant language in which it is written (e.g., English, Spanish, or French). It does not attempt to interpret meaning, opinion, or emotion; a negative complaint in English is treated identically to a positive one in the same language. Consequently, while it is useful for routing feedback to a translator, it cannot evaluate the tone of the content.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
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.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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