The answer is extractive summarization, the correct Azure AI Language feature for this task. This is because extractive summarization works by analyzing a document and identifying the most salient sentences, then extracting them verbatim to form a concise summary, which perfectly matches the legal firm’s need to highlight key sentences from lengthy court rulings without generating new text. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the core distinction between extractive and abstractive summarization—a common trap is confusing the two, as abstractive summarization generates novel sentences rather than pulling existing ones. A helpful memory tip is to associate “extractive” with “extracting” existing text, like a highlighter picking out key lines from a page.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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 individual important words or short phrases, not a coherent summary of key points.
✗
Named entity recognition
Why it's wrong here
Named entity recognition identifies entities like people, places, and organizations but does not produce a summary.
✓
Extractive summarization
Why this is correct
Extractive summarization selects the most important sentences from the document to form a concise summary, which matches the requirement.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis detects the emotional tone (positive, negative, neutral), not key content or summary.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates 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.
Trap categories for this question
Keyword trap
Key phrase extraction returns individual important words or short phrases, not a coherent summary of key points.
Detailed technical explanation
How to think about this question
Extractive summarization in Azure AI Language uses a transformer-based ranking model that scores each sentence in the document based on relevance and importance, then selects the top-ranked sentences to form the summary. The feature supports both single-document and multi-document summarization, with a configurable maximum summary length (e.g., up to 20% of original text). In practice, this is ideal for legal or research use cases where preserving original wording is critical for accuracy.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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