The answer is key phrase extraction, because it automatically identifies the main topics or themes in a document without requiring any labeled training data. This built-in Azure AI Language feature analyzes unstructured text and returns a list of key phrases—such as ‘politics’, ‘economy’, or ‘sports’—that represent the document’s central ideas, making it ideal for the news agency’s need to determine main topics without manual tagging. On the AI-900 exam, this question tests your understanding of Azure AI Language’s prebuilt capabilities, specifically distinguishing key phrase extraction from other features like entity recognition or sentiment analysis. A common trap is confusing it with named entity recognition, which extracts specific people, places, or organizations rather than overarching themes. Remember: key phrase extraction is for *themes* and *topics*, not specific entities. For a quick memory tip, think “Key phrases = Key topics,” and recall that it works out-of-the-box with zero training data.
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 news agency publishes hundreds of articles daily. They want to automatically determine the main topics discussed in each article, such as 'politics', 'economy', or 'sports', without manually tagging them. The agency has no labeled training data. Which built-in 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
✓
Key phrase extraction
Key phrase extraction is the correct choice because it automatically identifies the main topics or themes in a document without requiring any labeled training data. The news agency can use this built-in Azure AI Language feature to extract key phrases like 'politics', 'economy', or 'sports' from each article, enabling automatic topic categorization without manual tagging.
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.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis evaluates whether text is positive, negative, or neutral, not the topics discussed.
✓
Key phrase extraction
Why this is correct
Key phrase extraction extracts the main topics or key points from text without requiring training data.
Related concept
Read the scenario before looking for a memorised answer.
✗
Named entity recognition
Why it's wrong here
Named entity recognition identifies specific named entities like people, organizations, and locations, not general topics.
✗
Language detection
Why it's wrong here
Language detection identifies the language of the text, not its topics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse key phrase extraction with named entity recognition, but NER extracts specific named entities (e.g., 'Microsoft', 'Seattle') rather than general topic phrases, making it unsuitable for identifying broad themes like 'politics' or 'sports'.
Detailed technical explanation
How to think about this question
Key phrase extraction in Azure AI Language uses a statistical model based on TF-IDF (Term Frequency-Inverse Document Frequency) and graph-based ranking algorithms to identify the most salient phrases in a document. It returns a list of key phrases ranked by relevance, making it ideal for unsupervised topic discovery. In a real-world scenario, a news agency could batch-process hundreds of articles daily via the REST API, extracting key phrases like 'presidential election' or 'stock market rally' to automatically tag content for search and recommendation systems.
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: Key phrase extraction — Key phrase extraction is the correct choice because it automatically identifies the main topics or themes in a document without requiring any labeled training data. The news agency can use this built-in Azure AI Language feature to extract key phrases like 'politics', 'economy', or 'sports' from each article, enabling automatic topic categorization without manual tagging.
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.
About these practice questions
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Variation 1. A news agency publishes hundreds of articles daily. They want to automatically extract the main topics discussed in each article, such as 'politics', 'economy', or 'sports', to categorize content without manual tagging. Which built-in Azure AI Language feature should they use?
easy
✓ A.Key phrase extraction
B.Named entity recognition
C.Sentiment analysis
D.Language detection
Why A: Key phrase extraction is the correct choice because it identifies the main topics or subjects discussed in a document, such as 'politics', 'economy', or 'sports', without requiring manual tagging. This feature returns a list of key phrases that represent the core content of each article, directly addressing the need to automatically categorize content by topic.
Last reviewed: Jun 11, 2026
Question Discussion
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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.
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