The answer is Key Phrase Extraction, which is the correct Azure Text Analytics capability for automatically identifying the most important topics discussed in each article. This feature works by analyzing unstructured text and returning a list of key phrases that summarize the core content, making it ideal for topic identification and content categorization on a news platform. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of the distinct Text Analytics services, often presenting a trap where candidates confuse Key Phrase Extraction with Named Entity Recognition—remember that entities are specific people, places, or organizations, while key phrases capture broader themes and subjects. A useful memory tip is to think of Key Phrase Extraction as the tool that answers “what is this article about?” rather than “who or where is mentioned?”
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.
An online news platform receives thousands of articles daily. The editors want to automatically identify the most important topics discussed in each article to help with content categorization. Which Azure Text Analytics capability should they use?
Why wrong: Sentiment analysis evaluates the emotional tone of the text (positive, negative, neutral). It does not list the topics discussed.
B
Key Phrase Extraction
Key phrase extraction returns a list of the most important phrases or topics in the text. This directly matches the requirement to identify important topics from articles.
C
Named Entity Recognition
Why wrong: Named Entity Recognition identifies specific entities such as names of people, organizations, locations, dates, etc. It does not summarize the general topics of the article.
D
Language Detection
Why wrong: Language detection identifies the language in which the text is written. It does not provide any information about the content or topics.
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 (B) is the correct Azure Text Analytics capability because it identifies the most important topics and main points discussed in a document by returning a list of key phrases that summarize the core content. For an online news platform needing to automatically detect topics for categorization, this directly extracts the salient subjects from each article, unlike other capabilities that focus on sentiment, named entities, or language identification.
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 the emotional tone of the text (positive, negative, neutral). It does not list the topics discussed.
✓
Key Phrase Extraction
Why this is correct
Key phrase extraction returns a list of the most important phrases or topics in the text. This directly matches the requirement to identify important topics from articles.
Related concept
Read the scenario before looking for a memorised answer.
✗
Named Entity Recognition
Why it's wrong here
Named Entity Recognition identifies specific entities such as names of people, organizations, locations, dates, etc. It does not summarize the general topics of the article.
✗
Language Detection
Why it's wrong here
Language detection identifies the language in which the text is written. It does not provide any information about the content or topics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Named Entity Recognition (C) with topic extraction, because both deal with 'important' items in text, but NER focuses on specific named entities (e.g., 'Microsoft', 'New York') rather than the overarching themes or key phrases that summarize the document's content.
Detailed technical explanation
How to think about this question
Under the hood, Azure's Key Phrase Extraction uses a machine learning model based on a text-rank algorithm or similar graph-based ranking methods, which analyze term frequency, co-occurrence, and syntactic dependencies to score and extract the most representative phrases. A subtle behavior is that the service returns phrases (e.g., 'climate change policy') rather than single words, and it can handle multi-word expressions that are critical for topic categorization in news articles. In a real-world scenario, an article about 'AI advancements in healthcare' would yield key phrases like 'AI advancements', 'healthcare', and 'machine learning models', enabling automated tagging without manual review.
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 (B) is the correct Azure Text Analytics capability because it identifies the most important topics and main points discussed in a document by returning a list of key phrases that summarize the core content. For an online news platform needing to automatically detect topics for categorization, this directly extracts the salient subjects from each article, unlike other capabilities that focus on sentiment, named entities, or language identification.
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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