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 legal firm needs to automatically produce a short summary of each lengthy court ruling, highlighting the most important sentences. Which Azure AI Language feature should they use?
⚠ Common exam trap
Candidates often confuse key phrase extraction (Option A) with extractive summarization, because both involve 'extracting' content, but key phrase extraction only yields isolated terms, not complete sentences forming a summary.
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
✓
Extractive summarization
Extractive summarization (Option C) is the correct Azure AI Language feature because it identifies and extracts the most important sentences from a document to produce a concise summary. This directly matches the legal firm's requirement to automatically generate a short summary of lengthy court rulings by highlighting key sentences, without generating new text.
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 uses statistical algorithms to return a list of standalone terms and short phrases that represent salient topics in the document, but it does not preserve grammatical structure or generate complete sentences. A legal firm's summary needs connected prose that expresses relationships and legal conclusions, not an isolated list of frequent or notable tokens. Thus key phrases are a useful preprocessing step, not a summarization solution.
When this WOULD be correct
A medical research team needs to automatically extract the most frequently mentioned medical terms (e.g., 'hypertension', 'diabetes') from patient notes to identify common conditions. Key phrase extraction would be correct because it outputs a list of relevant terms, not a summary.
- ✗
Named entity recognition
Why it's wrong here
Named entity recognition (NER) scans text to classify tokens such as people, organizations, locations, dates, and legal case numbers into predefined semantic types. While a legal summary might mention entities, NER only tags individual mentions; it cannot assess importance, order information, or synthesize those entities into a concise, sentence-level overview. Therefore it fails the core summarization goal.
When this WOULD be correct
A healthcare organization needs to automatically extract patient names, medication names, and diagnosis codes from clinical notes for data entry. Named entity recognition would be the correct feature to identify these entities.
- ✓
Extractive summarization
Why this is correct
Extractive summarization works by analyzing the source document and scoring each sentence for importance based on factors like frequency, position, and semantic relevance, then returning the top-ranking sentences verbatim in a logical order. For a legal firm that needs a concise summary of key points, this directly produces a coherent distilled statement while preserving the original wording and legal details.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis classifies text into positive, negative, or neutral polarity, often using sentiment scores and opinion mining to gauge attitudes. It is used for tasks like customer review analysis, social media monitoring, or market research, but it does not identify which content is most important or combine sentences into a summary. A legal summary must preserve facts and reasoning, not emotional tone, so this capability does not meet the requirement.
When this WOULD be correct
A company wants to automatically determine whether customer reviews of a product are generally positive or negative. Sentiment analysis would be the correct Azure AI Language feature to use.
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.
✓Extractive summarizationCorrect answer▾
Why this is correct
Extractive summarization works by analyzing the source document and scoring each sentence for importance based on factors like frequency, position, and semantic relevance, then returning the top-ranking sentences verbatim in a logical order. For a legal firm that needs a concise summary of key points, this directly produces a coherent distilled statement while preserving the original wording and legal details.
✗Key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Key phrase extraction identifies single words or short phrases (e.g., 'negligence', 'plaintiff'), but does not produce a coherent summary of multiple sentences. The question requires a short summary highlighting important sentences, which is extractive summarization.
★ When this WOULD be the correct answer
A medical research team needs to automatically extract the most frequently mentioned medical terms (e.g., 'hypertension', 'diabetes') from patient notes to identify common conditions. Key phrase extraction would be correct because it outputs a list of relevant terms, not a summary.
Why candidates choose this
Candidates may confuse 'key phrases' with 'key sentences' and assume that extracting important phrases is equivalent to summarizing the text, not realizing that summarization requires sentence-level extraction and coherence.
✗Named entity recognitionWrong answer — click to see why▾
Why this is wrong here
Named entity recognition identifies and categorizes entities (e.g., people, organizations) in text, but does not produce a summary or extract key sentences from a document.
★ When this WOULD be the correct answer
A healthcare organization needs to automatically extract patient names, medication names, and diagnosis codes from clinical notes for data entry. Named entity recognition would be the correct feature to identify these entities.
Why candidates choose this
Candidates may confuse 'extracting important sentences' with 'extracting entities,' as both involve extraction tasks, leading them to choose named entity recognition without understanding the summarization requirement.
✗Sentiment analysisWrong answer — click to see why▾
Why this is wrong here
Sentiment analysis detects positive, negative, or neutral sentiment in text, but the question requires summarizing key sentences from court rulings, which is a summarization task, not sentiment detection.
★ When this WOULD be the correct answer
A company wants to automatically determine whether customer reviews of a product are generally positive or negative. Sentiment analysis would be the correct Azure AI Language feature to use.
Why candidates choose this
Candidates may confuse sentiment analysis with summarization because both involve processing text, but sentiment analysis focuses on opinion polarity, not extracting important content.
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
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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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