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
An online news platform receives thousands of articles daily. The editors want to automatically identify the most important topics discussed in each article to help with content categorization. Which Azure Text Analytics capability should they use?
⚠ Common exam trap
Watch out — candidates often confuse Named Entity Recognition (C) with topic extraction, because both deal with 'important' items in text, but NER focuses on specific named entities (e.g., 'Microsoft', 'New York') rather than the overarching themes or key phrases that summarize the document's content.
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 (B) is the correct Azure Text Analytics capability because it identifies the most important topics and main points discussed in a document by returning a list of key phrases that summarize the core content. For an online news platform needing to automatically detect topics for categorization, this directly extracts the salient subjects from each article, unlike other capabilities that focus on sentiment, named entities, or language identification.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Sentiment Analysis
Why it's wrong here
Sentiment analysis evaluates the overall emotional tone of a text and assigns a score from positive to negative, possibly at both sentence and document level. It can tell you whether an article is celebratory, critical, or neutral, but it provides no vocabulary about the subject matter itself. Two articles about completely different topics can both return the same sentiment score, so this service cannot be used to identify important topics from thousands of incoming articles.
When this WOULD be correct
A company wants to automatically categorize customer feedback as positive, negative, or neutral to monitor brand perception. Sentiment Analysis would be the correct Azure Text Analytics capability for this task.
- ✓
Key Phrase Extraction
Why this is correct
Key phrase extraction is an Azure AI Language feature that returns a ranked list of the most salient words or multi-word expressions in a document. It uses statistical and semantic modeling to surface phrases like 'renewable energy adoption' or 'monetary policy,' which directly represent the important topics covered in an article. Because it is designed to summarize the main ideas rather than only detect tone, entities, or language, it is the correct service for automatically identifying what each daily article is about.
- ✗
Named Entity Recognition
Why it's wrong here
Named entity recognition (NER) finds and categorizes specific mentions such as people, organizations, locations, dates, and quantities, labeling each with a category and confidence. While it enriches metadata by telling you who and what appears in an article, it does not distill the core arguments or overarching topics. For example, an article about 'Google and Facebook' could focus on antitrust, privacy policy, or stock performance, and NER would only return the company names, not the central theme.
When this WOULD be correct
An exam question: 'A legal firm needs to automatically extract all mentions of company names, court names, and judge names from court documents. Which Azure Text Analytics capability should they use?'
- ✗
Language Detection
Why it's wrong here
Language detection identifies the predominant written language of a document by analyzing character sets, script patterns, and linguistic cues. Its only output is a language code (such as 'en' or 'es') and a confidence score, which helps route content or trigger localization. The service is completely blind to the underlying subject and themes, making it irrelevant when the goal is to extract the key topics discussed in each article.
When this WOULD be correct
A multinational company receives customer feedback in multiple languages and needs to route each feedback to the appropriate language-specific team for processing. Language Detection would be correct to automatically determine the language of each 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.
✓Key Phrase ExtractionCorrect answer▾
Why this is correct
Key phrase extraction is an Azure AI Language feature that returns a ranked list of the most salient words or multi-word expressions in a document. It uses statistical and semantic modeling to surface phrases like 'renewable energy adoption' or 'monetary policy,' which directly represent the important topics covered in an article. Because it is designed to summarize the main ideas rather than only detect tone, entities, or language, it is the correct service for automatically identifying what each daily article is about.
✗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 main topics. The question asks for identifying important topics, which is the purpose of Key Phrase Extraction.
★ When this WOULD be the correct answer
A company wants to automatically categorize customer feedback as positive, negative, or neutral to monitor brand perception. Sentiment Analysis would be the correct Azure Text Analytics capability for this task.
Why candidates choose this
Candidates may confuse 'topics' with 'sentiment' or think that identifying important topics involves understanding the overall opinion, but sentiment analysis does not extract topic-level information.
✗Named Entity RecognitionWrong answer — click to see why▾
Why this is wrong here
Named Entity Recognition identifies specific entities like people, organizations, and locations, but the question asks for the most important topics discussed in each article, which requires extracting key phrases that summarize the main subjects.
★ When this WOULD be the correct answer
An exam question: 'A legal firm needs to automatically extract all mentions of company names, court names, and judge names from court documents. Which Azure Text Analytics capability should they use?'
Why candidates choose this
Candidates may confuse 'topics' with 'entities', thinking that identifying named entities (like people or places) is equivalent to identifying the main topics of an article.
✗Language DetectionWrong answer — click to see why▾
Why this is wrong here
Language Detection identifies the language of text, not topics. The question asks for identifying important topics within articles, which is unrelated to language identification.
★ When this WOULD be the correct answer
A multinational company receives customer feedback in multiple languages and needs to route each feedback to the appropriate language-specific team for processing. Language Detection would be correct to automatically determine the language of each feedback.
Why candidates choose this
Candidates may confuse language detection with topic detection because both involve analyzing text content, but they serve fundamentally different purposes.
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
Machine Learning Core Concepts
Key term
Text analytics
Text analytics is the process of turning unstructured text, like emails or social media posts, into structured data that can be analyzed to find patterns, sentiments, and insights.
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
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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.