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. A key principle to apply: key phrase extraction identifies the main concepts or topics in text.. 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 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?
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 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.
Key principle: Key phrase extraction identifies the main concepts or topics in text.
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
Correct. Key phrase extraction returns a list of key phrases that represent the main topics or concepts in the text.
Related concept
Key phrase extraction identifies the main concepts or topics in text.
✗
Named entity recognition
Why it's wrong here
Incorrect. Named entity recognizes specific entities like persons, locations, and organizations, not general topics.
✗
Sentiment analysis
Why it's wrong here
Incorrect. Sentiment analysis determines the emotional tone (positive, negative, neutral) of text, not its topics.
✗
Language detection
Why it's wrong here
Incorrect. Language detection identifies the language of the text, not its content topics.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse named entity recognition (which extracts specific entities like 'Microsoft' or 'New York') with key phrase extraction (which extracts general topics like 'technology' or 'urban development'), leading them to choose option B incorrectly.
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 like TextRank to identify phrases that are most representative of the document's content. In Azure AI Language, the service returns a confidence score for each key phrase, allowing downstream systems to filter or prioritize topics. A real-world scenario is a news aggregator using key phrase extraction to automatically generate topic tags for millions of articles, enabling personalized content recommendations.
KKey Concepts to Remember
Key phrase extraction identifies the main concepts or topics in text.
It is part of the Azure AI Language service.
It helps in summarizing and categorizing content automatically.
The output is a list of representative noun phrases.
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
Key phrase extraction identifies the main concepts or topics in text.
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. Key phrase extraction identifies the main concepts or topics in text. 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.
Review key phrase extraction identifies the main concepts or topics in text., then practise related AI-900 questions on the same topic to reinforce the concept.
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Key phrase extraction identifies the main concepts or topics in text..
What is the correct answer to this question?
The correct answer is: Key phrase extraction — 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.
What should I do if I get this AI-900 question wrong?
Review key phrase extraction identifies the main concepts or topics in text., then practise related AI-900 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Key phrase extraction identifies the main concepts or topics in text.
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Question Discussion
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