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 marketing team wants to automatically analyze thousands of customer reviews to identify the most commonly discussed aspects, such as 'price', 'durability', or 'customer service'. They do not have any labeled data for custom training. Which prebuilt Azure AI Language feature should they use?
⚠ Common exam trap
Many exam-takers confuse 'key phrase extraction' with 'entity recognition', mistakenly thinking that named entities like 'price' or 'customer service' are entities, when in fact they are general concepts extracted as key phrases, not predefined entity categories.
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
✓
Key phrase extraction
Key phrase extraction is the correct choice because it automatically identifies the most important points or topics (like 'price', 'durability', 'customer service') from unstructured text without requiring any labeled training data. This prebuilt Azure AI Language feature is designed specifically to surface commonly discussed aspects from large volumes of text, making it ideal for analyzing thousands of customer reviews.
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 this is correct
Correct because key phrase extraction automatically identifies the most important words and phrases that summarize the main topics discussed in a document. It directly answers the need to find commonly discussed aspects like 'price' and 'durability'.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates the emotional tone of a document, assigning a polarity score from positive to negative and often an overall sentiment label, but it does not extract the subjects that the sentiment is attached to. A review stating 'great durability' and another stating 'terrible durability' are both about durability, yet sentiment analysis would only return positive/negative scores without ever identifying durability as the recurring topic. To discover which specific keywords customers mention, you need key phrase extraction, not polarity detection.
When this WOULD be correct
A question asking to automatically determine whether customer reviews express positive, negative, or neutral opinions about a product or service, without needing labeled data, would make sentiment analysis the correct answer.
- ✗
Entity recognition
Why it's wrong here
Named entity recognition (NER) identifies and classifies specific, predefined semantic categories, such as persons, organizations, locations, dates, and monetary values, based on proper nouns and fixed entity types. The concepts 'price' and 'durability' are common, abstract nouns that do not correspond to named entities, and NER is not designed to pick out general topic words or summarize the main discussion points of a collection of reviews. Even when NER captures product names or vendors, it does not produce a ranked list of the aspects customers most frequently talk about.
When this WOULD be correct
Entity recognition would be correct if the question asked to identify specific named entities from reviews, such as product names, company names, or locations (e.g., 'Contoso', 'Seattle'), especially when the goal is to extract structured data like brands or places from unstructured text.
- ✗
Language detection
Why it's wrong here
Language detection is a text classification technique that identifies the predominant language of a document, such as English, Spanish, or French, by analyzing character patterns and stop words. It does not read or interpret the meaning, topics, or semantic content of the text, so it cannot surface 'price' or 'durability' as commonly discussed aspects. While a useful preprocessing step, it gives zero insight into what the reviews actually talk about.
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.
✓Key phrase extractionCorrect answer▾
Why this is correct
Correct because key phrase extraction automatically identifies the most important words and phrases that summarize the main topics discussed in a document. It directly answers the need to find commonly discussed aspects like 'price' and 'durability'.
✗Sentiment analysisWrong answer — click to see why▾
Why this is wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not the extraction of commonly discussed aspects like 'price' or 'durability'.
★ When this WOULD be the correct answer
A question asking to automatically determine whether customer reviews express positive, negative, or neutral opinions about a product or service, without needing labeled data, would make sentiment analysis the correct answer.
Why candidates choose this
Candidates may confuse 'analyzing customer reviews' with sentiment analysis, assuming the goal is to gauge overall opinion rather than extract specific topics.
✗Entity recognitionWrong answer — click to see why▾
Why this is wrong here
Entity recognition identifies named entities like people, places, or organizations, not the general topics or aspects (e.g., 'price', 'durability') discussed in reviews. The question asks for commonly discussed aspects, which requires extracting key phrases, not named entities.
★ When this WOULD be the correct answer
Entity recognition would be correct if the question asked to identify specific named entities from reviews, such as product names, company names, or locations (e.g., 'Contoso', 'Seattle'), especially when the goal is to extract structured data like brands or places from unstructured text.
Why candidates choose this
Candidates may confuse 'entity' with 'aspect' or think that entity recognition can extract any important term, not realizing it is limited to predefined categories like person, location, organization, and does not handle general topic extraction.
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
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
Key phrase extraction
Key phrase extraction is an Azure AI service feature that automatically identifies and extracts the most important words and phrases from a piece of text.
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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.