The answer is Key Phrase Extraction. This prebuilt Azure AI Language feature is the correct choice because it automatically scans unstructured text—like thousands of news articles—and returns a ranked list of the main topics, concepts, or themes discussed, such as 'technology', 'politics', or 'sports', without requiring any custom training or labeled data. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between prebuilt Language features: Key Phrase Extraction is designed for identifying high-level subjects, while Named Entity Recognition (NER) focuses on specific entities like people or places, and Sentiment Analysis measures emotional tone. A common trap is confusing Key Phrase Extraction with NER, but remember: key phrases are broad topics, not specific names. For a quick memory tip, think "Key Phrase = Key Topics"—if you need the big-picture subject of an article, this is your go-to feature.
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 research team wants to automatically analyze thousands of online news articles to identify the main topics discussed in each article (e.g., 'technology', 'politics', 'sports'). They need a prebuilt Azure AI Language feature that returns a list of key concepts or topics without any custom training. Which feature should they use?
Key phrase extraction identifies the most important phrases (e.g., 'machine learning', 'climate change') that summarize the main topics of the article.
B
Named Entity Recognition (NER)
Why wrong: NER extracts specific named entities such as person names, locations, and organizations, not general topic-level concepts.
C
Sentiment analysis
Why wrong: Sentiment analysis determines whether the text expresses positive, negative, or neutral sentiment, not the topics discussed.
D
Language detection
Why wrong: Language detection identifies the language the article is written in (e.g., English, French), not the topics it covers.
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 Azure AI Language feature because it automatically identifies and returns a list of the main topics, concepts, or themes discussed in a document without requiring any custom training or labeled data. This prebuilt capability is designed specifically for extracting high-level topics from unstructured text, making it ideal for analyzing thousands of news articles to determine subjects like 'technology', 'politics', or 'sports'.
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 this is correct
Key phrase extraction identifies the most important phrases (e.g., 'machine learning', 'climate change') that summarize the main topics of the article.
Related concept
Read the scenario before looking for a memorised answer.
✗
Named Entity Recognition (NER)
Why it's wrong here
NER extracts specific named entities such as person names, locations, and organizations, not general topic-level concepts.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines whether the text expresses positive, negative, or neutral sentiment, not the topics discussed.
✗
Language detection
Why it's wrong here
Language detection identifies the language the article is written in (e.g., English, French), not the topics it covers.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Named Entity Recognition (NER) with key phrase extraction, mistakenly thinking NER identifies topics when it actually extracts specific named entities like 'Microsoft' or 'New York', not general themes.
Detailed technical explanation
How to think about this question
Key phrase extraction 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 ranked list of key phrases, each with a relevance score, and can process up to 5,120 characters per document in a single API call. In a real-world scenario, a news aggregator could use this feature to automatically tag articles with topics like 'climate change' or 'election' without manual intervention.
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 Azure AI Language feature because it automatically identifies and returns a list of the main topics, concepts, or themes discussed in a document without requiring any custom training or labeled data. This prebuilt capability is designed specifically for extracting high-level topics from unstructured text, making it ideal for analyzing thousands of news articles to determine subjects like 'technology', 'politics', or 'sports'.
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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