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 marketing team needs to analyze thousands of product reviews to identify the most frequently mentioned topics, such as 'battery life', 'customer support', and 'price'. They want an automated method to extract these main concepts from each review. Which Azure AI Language feature should they use?
Why wrong: Language detection identifies the language used in the text (e.g., English, Spanish), not the topics discussed.
B
Sentiment analysis
Why wrong: Sentiment analysis determines the emotional tone (positive, negative, neutral) of the text but does not extract the topics or concepts themselves.
C
Key phrase extraction
Key phrase extraction identifies and returns a list of important phrases from the text that represent the main concepts or topics discussed, such as 'battery life' or 'customer support'.
D
Entity recognition
Why wrong: Entity recognition extracts named entities like people, organizations, dates, or locations. It does not extract broader topic phrases like 'battery life'.
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 concepts, such as 'battery life', 'customer support', and 'price', from unstructured text like product reviews. This feature is specifically designed to extract the most salient topics or points from a document, making it ideal for analyzing thousands of reviews to find frequently mentioned themes.
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.
✗
Language detection
Why it's wrong here
Language detection identifies the language used in the text (e.g., English, Spanish), not the topics discussed.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines the emotional tone (positive, negative, neutral) of the text but does not extract the topics or concepts themselves.
✓
Key phrase extraction
Why this is correct
Key phrase extraction identifies and returns a list of important phrases from the text that represent the main concepts or topics discussed, such as 'battery life' or 'customer support'.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity recognition
Why it's wrong here
Entity recognition extracts named entities like people, organizations, dates, or locations. It does not extract broader topic phrases like 'battery life'.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing key phrase extraction with entity recognition, as both extract information from text, but entity recognition is limited to predefined categories (e.g., person, location) while key phrase extraction captures any salient concept.
Trap categories for this question
Keyword trap
Entity recognition extracts named entities like people, organizations, dates, or locations. It does not extract broader topic phrases like 'battery life'.
Detailed technical explanation
How to think about this question
Key phrase extraction uses a statistical model based on a large corpus of text to identify words and phrases that are most representative of the document's content. It does not rely on a predefined list of entities, making it flexible for domain-specific topics. In a real-world scenario, a marketing team could use this to aggregate key phrases across thousands of reviews and then perform frequency analysis to identify the most common customer concerns.
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 concepts, such as 'battery life', 'customer support', and 'price', from unstructured text like product reviews. This feature is specifically designed to extract the most salient topics or points from a document, making it ideal for analyzing thousands of reviews to find frequently mentioned themes.
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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