- A
Text Analytics for Health
Why wrong: This feature is designed for extracting medical entities and relationships from clinical notes, not for general topic extraction from product reviews.
- B
Key Phrase Extraction
Key Phrase Extraction identifies the main points or topics in text, making it suitable for extracting frequently mentioned aspects from customer reviews without custom training.
- C
Conversational Language Understanding
Why wrong: This feature requires custom training on user intents and entities for building task-oriented chatbots, not for extracting topics from static text.
- D
Entity Linking
Why wrong: Entity Linking disambiguates named entities by linking them to a knowledge base (e.g., Wikipedia), which is not designed for general topic extraction.
Quick Answer
Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature that automatically identifies and surfaces the main topics or aspects from unstructured text, such as product reviews, without requiring any custom training or labeled data. It works by analyzing linguistic patterns and term frequency to pull out frequently mentioned concepts like 'price', 'durability', and 'customer service', making it ideal for summarization tasks where you need to understand what customers are talking about at scale. On the AI-900 exam, this question tests your ability to distinguish between prebuilt text analysis features: Key Phrase Extraction is for pulling out main topics, while Entity Recognition identifies specific named items like people, places, or organizations—a common trap is confusing the two. Remember the memory tip: "Key phrases are the 'what' (topics), entities are the 'who' and 'where' (specific names)."
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 customer insights team at a retail company has collected thousands of product reviews. They want to automatically extract the most frequently mentioned topics or aspects from these reviews, such as 'price', 'durability', and 'customer service', without any custom training. Which prebuilt Azure AI Language feature should they use?
Answer choices
Why each option matters
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 is a prebuilt Azure AI Language feature designed to automatically identify and extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.
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.
- ✗
Text Analytics for Health
Why it's wrong here
This feature is designed for extracting medical entities and relationships from clinical notes, not for general topic extraction from product reviews.
- ✓
Key Phrase Extraction
Why this is correct
Key Phrase Extraction identifies the main points or topics in text, making it suitable for extracting frequently mentioned aspects from customer reviews without custom training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Conversational Language Understanding
Why it's wrong here
This feature requires custom training on user intents and entities for building task-oriented chatbots, not for extracting topics from static text.
- ✗
Entity Linking
Why it's wrong here
Entity Linking disambiguates named entities by linking them to a knowledge base (e.g., Wikipedia), which is not designed for general topic extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Key Phrase Extraction with Entity Linking or Conversational Language Understanding, mistakenly thinking that extracting topics requires custom training or entity disambiguation, when in fact Key Phrase Extraction is a zero-shot, prebuilt feature specifically designed for this exact use case.
Detailed technical explanation
How to think about this question
Key Phrase Extraction uses a statistical natural language processing model that identifies noun phrases and other salient terms by analyzing term frequency, co-occurrence patterns, and part-of-speech tags, without requiring any custom training. Under the hood, it leverages a pre-trained transformer-based model that scores phrases based on their relevance to the document's overall meaning, returning a ranked list of key phrases. In a real-world scenario, a retail company could feed thousands of reviews into the API and receive a consolidated list of top topics like 'battery life' or 'comfort', which can then be used for sentiment analysis or trend reporting.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 is a prebuilt Azure AI Language feature designed to automatically identify and extract the main points or topics from unstructured text, such as product reviews, without requiring any custom training or labeled data. It surfaces frequently mentioned aspects like 'price', 'durability', and 'customer service' by analyzing linguistic patterns and term frequency, making it ideal for this summarization task.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A retail company collects thousands of customer reviews. They want to automatically extract frequently mentioned aspects (e.g., 'battery life', 'customer service', 'price') to understand common topics. Which Azure AI Language capability should they use?
medium- A.Sentiment analysis
- ✓ B.Key phrase extraction
- C.Named entity recognition
- D.Language detection
Why B: Key phrase extraction is the correct Azure AI Language capability because it is specifically designed to identify and extract the main talking points or topics from unstructured text, such as 'battery life', 'customer service', and 'price' from customer reviews. This directly matches the requirement to automatically extract frequently mentioned aspects without needing predefined categories.
Last reviewed: Jun 11, 2026
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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