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 news agency wants to build a system that can automatically generate a short headline for each news article. The system should select the most important sentence from the article as the headline. Which Azure AI Language feature should they use?
⚠ Common exam trap
Candidates often confuse key phrase extraction with extractive summarization, assuming that extracting 'key phrases' is the same as extracting sentences, but key phrase extraction yields only isolated words or short phrases, not complete, coherent sentences suitable for a headline.
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
✓
B: Extractive summarization
Extractive summarization is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document, which directly matches the requirement to select the most important sentence as a headline. Unlike key phrase extraction, which returns individual words or short phrases, extractive summarization returns complete sentences ranked by relevance, making it ideal for headline generation.
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 returns isolated terms or short phrases—such as 'stock market' or 'interest rates'—that represent the dominant topics, but it does not arrange them into a grammatically valid sentence. A headline must be a complete, readable clause with a clear structure, and a bag of key phrases lacks subject-verb-object ordering and punctuation. Consequently, the feature cannot produce sentence-level text that would satisfy the news agency's need for automatic headline generation.
- ✓
B: Extractive summarization
Why this is correct
Extractive summarization analyzes the source text and assigns each sentence a relevance score based on factors like word frequency, sentence position, and centrality to the document's main theme, then selects the top-scoring sentences verbatim. Because it pulls existing sentences, the output preserves grammaticality and original phrasing, which makes the chosen sentences directly usable as headline candidates or concise summaries. This is exactly the capability needed to automatically pick out complete sentences that convey the news event.
- ✗
C: Entity recognition
Why it's wrong here
Named entity recognition (NER) locates and classifies spans such as company names, people, dates, and locations, returning them as labeled chunks like 'Microsoft' or 'January 15, 2025.' It does not evaluate whole sentences for importance, nor does it generate any sentence-level output that could serve as a headline. Even if all entities are extracted, the result is a list of discrete references with no syntactic connection, so the system still lacks a readable summary.
- ✗
D: Sentiment analysis
Why it's wrong here
Sentiment analysis measures the emotional tone of the text, assigning polarity scores such as positive, negative, or neutral to a sentence or document. This tells the agency whether an article is favorable or critical, but it never ranks sentences by informational importance or extracts a central statement. Since the goal is to produce headline-worthy sentences rather than gauge emotion, sentiment analysis cannot fulfill the requirement.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
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.
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.
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