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 customer support team receives hundreds of long product reviews every day. They want to automatically summarize each review into a few key sentences to quickly understand the main points. Which prebuilt Azure AI Language feature should they use?
⚠ Common exam trap
Many exam-takers confuse key phrase extraction with summarization, assuming that extracting key phrases is sufficient to summarize a review, but key phrases are not sentences and cannot convey the main points in a readable, coherent form.
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 is the correct choice because it is specifically designed to condense long documents into a few key sentences by extracting the most important sentences directly from the original text. This aligns perfectly with the customer support team's goal of automatically summarizing hundreds of product reviews into concise, key points for quick understanding.
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 returns a flat list of significant words and phrases (e.g., 'battery life', 'shipping delay') without any syntactic connection between them. Unlike extractive summarization, it never forms complete, grammatical sentences, so it cannot produce a readable narrative that captures the review's argument. A support agent would have to infer relationships between the phrases, which is error-prone and inefficient for triaging hundreds of long reviews.
When this WOULD be correct
A company wants to automatically tag product reviews with the most frequently mentioned features (e.g., 'price', 'durability', 'design') to populate a searchable database. Key phrase extraction would be the correct choice because it extracts specific terms, not sentences.
- ✗
Sentiment analysis
Why it's wrong here
Sentiment analysis assigns a polarity score—positive, negative, or neutral—to either the entire document or individual sentences, often with confidence percentages. While this can indicate the overall tone of a product review, it does not identify which specific features were praised or criticized, nor does it generate any text that summarizes the content. A negative score alone fails to tell the support team whether the problem is a defect, a usability issue, or a pricing complaint.
When this WOULD be correct
A company wants to automatically classify customer feedback as positive, negative, or neutral to track overall satisfaction trends. In that scenario, sentiment analysis would be the correct Azure AI Language feature.
- ✓
Extractive summarization
Why this is correct
Extractive summarization selects the most important sentences directly from the source document and concatenates them into a coherent summary, preserving the original wording. In Azure AI Language, this prebuilt capability ranks sentences by salience using features like sentence position, term frequency, and semantic similarity, then returns the top-scoring sentences. For a support team processing lengthy product reviews, this yields a concise yet faithful condensation of the main points without paraphrasing or losing factual detail.
- ✗
Entity recognition
Why it's wrong here
Entity recognition, also known as named entity recognition (NER), detects and categorizes pre-defined types such as persons, organizations, locations, dates, and quantities in the text. For product reviews, it might extract entities like 'Acme Corp' or 'Model X2', but it does not attempt to capture the main ideas or produce a condensed version of the document. The output is a set of labeled spans, not a summary, so it cannot help a support team quickly understand the overall feedback contained in hundreds of long reviews.
When this WOULD be correct
A question asking: 'Which Azure AI Language feature should be used to extract all product names, dates, and company names from customer feedback?' would make entity recognition the correct answer, as it specializes in identifying such entities.
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 selects the most important sentences directly from the source document and concatenates them into a coherent summary, preserving the original wording. In Azure AI Language, this prebuilt capability ranks sentences by salience using features like sentence position, term frequency, and semantic similarity, then returns the top-scoring sentences. For a support team processing lengthy product reviews, this yields a concise yet faithful condensation of the main points without paraphrasing or losing factual detail.
✗Key phrase extractionWrong answer — click to see why▾
Why this is wrong here
Key phrase extraction identifies individual words or short phrases (e.g., 'battery life', 'customer service'), but does not generate coherent sentences summarizing the review. The question requires summarizing each review into key sentences, which is the task of extractive summarization.
★ When this WOULD be the correct answer
A company wants to automatically tag product reviews with the most frequently mentioned features (e.g., 'price', 'durability', 'design') to populate a searchable database. Key phrase extraction would be the correct choice because it extracts specific terms, not sentences.
Why candidates choose this
Candidates may confuse 'key phrases' with 'key sentences' and assume that extracting important phrases is equivalent to summarizing the main points, not realizing that summarization requires sentence-level extraction.
✗Sentiment analysisWrong answer — click to see why▾
Why this is wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, but does not produce a summary of key points. The question asks for summarizing reviews into key sentences, which requires extractive summarization, not sentiment detection.
★ When this WOULD be the correct answer
A company wants to automatically classify customer feedback as positive, negative, or neutral to track overall satisfaction trends. In that scenario, sentiment analysis would be the correct Azure AI Language feature.
Why candidates choose this
Candidates may confuse sentiment analysis with summarization because both involve processing text to extract meaning, and sentiment analysis is a more familiar concept, leading them to overlook the specific requirement for summary generation.
✗Entity recognitionWrong answer — click to see why▾
Why this is wrong here
Entity recognition identifies and categorizes named entities (e.g., people, organizations) in text, but does not summarize content. The question requires condensing reviews into key sentences, which is a summarization task, not entity extraction.
★ When this WOULD be the correct answer
A question asking: 'Which Azure AI Language feature should be used to extract all product names, dates, and company names from customer feedback?' would make entity recognition the correct answer, as it specializes in identifying such entities.
Why candidates choose this
Candidates may confuse entity recognition with extracting key information, mistakenly thinking that identifying entities like product names or features is equivalent to summarizing the main points of a review.
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?”
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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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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.