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 news agency publishes hundreds of articles daily. They want to automatically extract the main topics discussed in each article, such as 'politics', 'economy', or 'sports', to categorize content without manual tagging. Which built-in Azure AI Language feature should they use?
⚠ Common exam trap
Many exam-takers confuse named entity recognition (which extracts specific entities like 'Microsoft' or 'New York') with key phrase extraction (which extracts general topics like 'technology' or 'urban development'), leading them to choose option B incorrectly.
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 identifies the main topics or subjects discussed in a document, such as 'politics', 'economy', or 'sports', without requiring manual tagging. This feature returns a list of key phrases that represent the core content of each article, directly addressing the need to automatically categorize content by topic.
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
Key phrase extraction is the correct choice because it uses statistical and NLP models to surface the most salient multi-word expressions in a document, such as 'breaking news' or 'economic crisis,' thereby summarizing the article's main topics. Unlike NER or sentiment analysis, it is specifically designed to return broad topical concepts rather than specific entities or emotional tone, making it ideal for automatically tagging hundreds of daily articles for cataloging and search.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition is incorrect for this use case because it identifies and classifies specific, discrete references such as person names, organizations, locations, dates, and quantities (e.g., 'Jane Doe,' 'United Nations,' '2025'). While useful for extracting structured data points from a news article, it does not summarize or infer the article's overarching topic; a list of entities may appear in a story about any subject, so it fails to meet the agency's goal of general topic labeling.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis is incorrect because it evaluates the emotional tone of the text, producing a polarity score ranging from negative to positive (or neutral), often used for gauging public opinion, brand perception, or social media mood. In a news aggregation context, the agency needs to identify what the article is about—its subject or theme—not whether the content expresses a favorable or unfavorable attitude. Therefore, sentiment analysis would provide irrelevant metadata for topic-based indexing and tagging.
- ✗
Language detection
Why it's wrong here
Language detection is incorrect because it determines the natural language in which the text is written (e.g., English, Spanish, French) by analyzing character frequency and lexicon patterns. For a news agency that already knows the language of its articles—or needs topic-based categorization regardless of language—this capability offers no insight into the content's subject matter. Detecting that an article is, say, English tells you nothing about its key topics, so it cannot fulfill the requirement of extracting topical summaries.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
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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