- A
Azure AI Language only supports extractive summarisation — abstractive requires Azure OpenAI
Why wrong: Azure AI Language supports both modes — abstractive summarisation is available as a pre-built feature in the Language service.
- B
Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation
Both modes are available — extractive quotes source sentences; abstractive generates new text capturing the meaning.
- C
Azure AI Language only supports abstractive summarisation because it is more advanced
Why wrong: Both modes are supported — extractive is simpler but more factually reliable; abstractive is more fluent.
- D
Extractive is for short texts; abstractive is required for documents longer than 10,000 words
Why wrong: Document length doesn't determine which mode to use — both can handle documents of various lengths.
Quick Answer
The correct answer is that Azure AI Language supports both extractive and abstractive summarization. Extractive summarization works by identifying and pulling the most important sentences directly from the source text, while abstractive summarization generates entirely new sentences that synthesize and rephrase the original content into a concise summary. This distinction matters because the Azure AI Language service offers both capabilities, giving you the flexibility to choose based on whether you need verbatim key points or a fluid, human-like restatement. On the AI-900 exam, this topic tests your understanding of natural language processing features within Azure AI, and a common trap is assuming the service only supports one type—remember, it provides both. For a quick memory tip, think of extractive as “extracting highlights” and abstractive as “abstracting the essence.”
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.
What is 'extractive vs abstractive summarisation' and which does Azure AI Language's document summarisation feature support?
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
Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation
Azure AI Language's document summarization feature supports both extractive summarization (selecting key sentences from the original text) and abstractive summarization (generating a new, condensed summary that rephrases the content). Option B is correct because the service provides both capabilities, allowing users to choose based on their needs.
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.
- ✗
Azure AI Language only supports extractive summarisation — abstractive requires Azure OpenAI
Why it's wrong here
Azure AI Language supports both modes — abstractive summarisation is available as a pre-built feature in the Language service.
- ✓
Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation
Why this is correct
Both modes are available — extractive quotes source sentences; abstractive generates new text capturing the meaning.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Azure AI Language only supports abstractive summarisation because it is more advanced
Why it's wrong here
Both modes are supported — extractive is simpler but more factually reliable; abstractive is more fluent.
- ✗
Extractive is for short texts; abstractive is required for documents longer than 10,000 words
Why it's wrong here
Document length doesn't determine which mode to use — both can handle documents of various lengths.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume abstractive summarization requires a separate service like Azure OpenAI, but Azure AI Language includes it natively, and they may also confuse the two types based on text length rather than the underlying technique.
Detailed technical explanation
How to think about this question
Under the hood, extractive summarization in Azure AI Language uses a ranking model to score sentences based on relevance and coherence, then selects the top-scoring ones. Abstractive summarization leverages transformer-based neural networks to generate novel sentences that capture the essence of the document, often using attention mechanisms to paraphrase and condense information. A real-world scenario where this matters is in legal document review, where extractive summarization preserves exact wording for compliance, while abstractive summarization provides a more readable overview for executives.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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: Azure AI Language supports both extractive (key sentences) and abstractive (generated synthesis) summarisation — Azure AI Language's document summarization feature supports both extractive summarization (selecting key sentences from the original text) and abstractive summarization (generating a new, condensed summary that rephrases the content). Option B is correct because the service provides both capabilities, allowing users to choose based on their needs.
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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Same concept, more angles
3 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. What is 'abstractive summarization' vs. 'extractive summarization' in Azure AI Language, and which produces summaries in new words?
medium- A.Extractive produces new words; abstractive copies sentences
- ✓ B.Abstractive generates new sentences; extractive selects existing sentences from the source
- C.They produce identical output through different computational paths
- D.Abstractive works only for legal documents; extractive for general text
Why B: Abstractive summarization generates new sentences that rephrase the core meaning of the source text, similar to how a human would summarize. Extractive summarization, in contrast, selects and copies key sentences directly from the original document without rewording them. Option B correctly identifies that abstractive produces new sentences while extractive selects existing ones.
Variation 2. What is the difference between extractive summarization and abstractive summarization?
medium- A.Extractive works on text; abstractive works on images
- ✓ B.Extractive pulls existing sentences; abstractive generates new text capturing the meaning
- C.Extractive is for long documents; abstractive is for short text
- D.Extractive summarization is always less accurate than abstractive
Why B: Option B is correct because extractive summarization identifies and extracts the most important sentences directly from the source text, while abstractive summarization generates new sentences that capture the core meaning, often using natural language generation techniques. In Azure AI Language, extractive summarization returns a set of ranked sentences from the original document, whereas abstractive summarization produces a concise summary that may rephrase content. This distinction is fundamental to understanding how different NLP workloads handle text summarization tasks.
Variation 3. What is 'abstractive summarisation' and how does it differ from 'extractive summarisation'?
medium- A.Extractive writes shorter summaries; abstractive writes longer ones
- ✓ B.Extractive selects key sentences verbatim; abstractive generates new sentences capturing the meaning
- C.Abstractive summarisation is only available for non-English languages
- D.Extractive summarisation uses generative AI; abstractive uses keyword ranking
Why B: Option B is correct because extractive summarisation works by selecting and concatenating the most important sentences directly from the source text, while abstractive summarisation uses natural language generation (NLG) models to produce entirely new sentences that paraphrase and condense the core meaning. This distinction is fundamental in Azure AI Language's summarisation capabilities, where extractive returns verbatim excerpts and abstractive generates novel, coherent summaries.
Last reviewed: Jun 11, 2026
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