Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A retail company wants to automatically determine whether customer reviews are positive, negative, or neutral. Which prebuilt Azure AI Language feature should they use?
⚠ Common exam trap
Many exam-takers confuse key phrase extraction with sentiment analysis because both deal with text content, but key phrase extraction only identifies terms without any polarity assessment, while sentiment analysis explicitly classifies emotional tone.
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
✓
Sentiment analysis
Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text into positive, negative, or neutral sentiment categories. This prebuilt capability analyzes customer reviews at the document and sentence level, returning a sentiment label and confidence scores, which directly meets the requirement of automatically determining review 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.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction is incorrect because it extracts salient nouns, verbs, and descriptive phrases—such as "delivery time" or "customer service"—to summarize the main topics mentioned in the text. It does not assign any sentiment polarity or value to those phrases; it can reveal what customers are talking about, but it cannot indicate whether the tone is favorable or unfavorable.
When this WOULD be correct
A company wants to automatically extract the main topics or important terms from customer feedback to identify common themes. In that case, key phrase extraction would be the correct feature.
- ✗
Language detection
Why it's wrong here
Language detection is incorrect because it only identifies the dominant language of a text (e.g., English, Spanish) by analyzing character patterns and scripts. It assigns no polarity or emotional evaluation, so even if the retail company knew a review was in English, it would still have no insight into whether the customer's sentiment was positive or negative.
When this WOULD be correct
A company receives customer feedback in multiple languages and needs to route each review to the appropriate language-specific processing pipeline. Language detection would be used to identify the language of each review.
- ✓
Sentiment analysis
Why this is correct
Sentiment analysis is the correct Azure AI Language feature because it uses machine learning models to assign a polarity label (positive, negative, or neutral) and confidence scores to text at both the document and sentence level. It is specifically designed to detect subjective opinions and emotions, such as determining whether a retail customer's review is satisfied or dissatisfied, which directly matches the scenario.
- ✗
Entity recognition
Why it's wrong here
Entity recognition is incorrect because it finds and categorizes named entities like organizations, locations, people, dates, and products (e.g., "Contoso Ltd." or "Store #42"), labeling them by predefined types. It performs no sentiment analysis, so a sentence mentioning "refund" or "quality" might be positive or negative depending on context, which this feature would not infer.
When this WOULD be correct
A question asking which Azure AI Language feature extracts names of people, organizations, or locations from text, such as 'Which feature should be used to identify product names in customer feedback?'
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Sentiment analysisCorrect answer▾
Why this is correct
Sentiment analysis is the correct Azure AI Language feature because it uses machine learning models to assign a polarity label (positive, negative, or neutral) and confidence scores to text at both the document and sentence level. It is specifically designed to detect subjective opinions and emotions, such as determining whether a retail customer's review is satisfied or dissatisfied, which directly matches the scenario.
✗Key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Key phrase extraction identifies important terms but does not classify sentiment (positive/negative/neutral), which is the explicit requirement.
★ When this WOULD be the correct answer
A company wants to automatically extract the main topics or important terms from customer feedback to identify common themes. In that case, key phrase extraction would be the correct feature.
Why candidates choose this
Candidates may confuse extracting key phrases with analyzing sentiment, thinking that key phrases like 'great' or 'terrible' directly indicate sentiment, but the feature does not perform classification.
✗Language detectionWrong answer — click to see why▾
Why this is wrong here
Language detection identifies the language of text (e.g., English, Spanish), not the sentiment (positive, negative, neutral) expressed in customer reviews.
★ When this WOULD be the correct answer
A company receives customer feedback in multiple languages and needs to route each review to the appropriate language-specific processing pipeline. Language detection would be used to identify the language of each review.
Why candidates choose this
Candidates may confuse 'language detection' with 'sentiment analysis' because both involve analyzing text, but they serve different purposes—detecting language versus detecting opinion polarity.
✗Entity recognitionWrong answer — click to see why▾
Why this is wrong here
Entity recognition identifies named entities like people, places, or products, but does not classify the sentiment (positive, negative, neutral) of customer reviews.
★ When this WOULD be the correct answer
A question asking which Azure AI Language feature extracts names of people, organizations, or locations from text, such as 'Which feature should be used to identify product names in customer feedback?'
Why candidates choose this
Candidates may confuse entity recognition with sentiment analysis because both involve analyzing text, but entity recognition focuses on extracting specific items rather than overall tone.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Label
A label is a piece of metadata attached to data, objects, or resources to identify, classify, or describe them for easier management and retrieval.
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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Senior Network & Security Engineer · founder of Courseiva
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