The answer is Key Phrase Extraction, a prebuilt Azure AI Language feature that automatically identifies and returns the main topics and recurring concepts from unstructured text, such as 'product quality', 'shipping delay', and 'customer service'. This feature performs key phrase extraction for summarizing topics from customer feedback by analyzing the text and surfacing the most salient terms without any custom training or labeled data, making it an out-of-the-box solution for quickly summarizing large collections. On the AI-900 exam, this question tests your ability to distinguish between prebuilt features like Key Phrase Extraction and custom-trained models like Custom Text Classification or Custom Named Entity Recognition—a common trap is confusing it with Entity Recognition, which extracts specific names or dates rather than overarching themes. A helpful memory tip: think of Key Phrase Extraction as the "topic highlighter" that pulls out the big-picture ideas from a sea of words, perfect for a business analyst needing instant insight without building a model.
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 business analyst wants to quickly summarize the main topics discussed in a large collection of customer feedback emails. The analyst needs to identify recurring concepts such as 'product quality', 'shipping delay', and 'customer service'. They want to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?
Why wrong: NER identifies and categorizes entities such as person names, locations, and organizations. It does not extract general key phrases or topics like 'product quality'.
B
Key Phrase Extraction
Correct. Key Phrase Extraction returns a list of key phrases from the text that capture the main topics, such as 'product quality' or 'shipping delay'. It is a prebuilt feature and requires no custom training.
C
Language Detection
Why wrong: Language Detection identifies the language of the text. It does not extract topics or key phrases.
D
Sentiment Analysis
Why wrong: Sentiment Analysis determines the overall sentiment (positive, negative, neutral) of the text. It does not extract specific topics or key phrases.
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 return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.
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.
✗
Named Entity Recognition (NER)
Why it's wrong here
NER identifies and categorizes entities such as person names, locations, and organizations. It does not extract general key phrases or topics like 'product quality'.
✓
Key Phrase Extraction
Why this is correct
Correct. Key Phrase Extraction returns a list of key phrases from the text that capture the main topics, such as 'product quality' or 'shipping delay'. It is a prebuilt feature and requires no custom training.
Related concept
Read the scenario before looking for a memorised answer.
✗
Language Detection
Why it's wrong here
Language Detection identifies the language of the text. It does not extract topics or key phrases.
✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis determines the overall sentiment (positive, negative, neutral) of the text. It does not extract specific topics or key phrases.
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 can extract general topics, when in fact NER is strictly limited to predefined entity categories like persons, locations, and organizations, not abstract recurring concepts.
Trap categories for this question
Keyword trap
NER identifies and categorizes entities such as person names, locations, and organizations. It does not extract general key phrases or topics like 'product quality'.
Detailed technical explanation
How to think about this question
Key Phrase Extraction uses a statistical natural language processing model that analyzes term frequency, co-occurrence patterns, and contextual relevance to surface the most salient phrases. Under the hood, it leverages Azure's pre-trained transformer-based models that have been fine-tuned on large corpora to distinguish between noise and meaningful concepts. In a real-world scenario, this feature can process thousands of emails in seconds, returning a ranked list of key phrases that directly map to business concerns like 'shipping delay' or 'customer service', without any custom labeling or training.
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 is a prebuilt Azure AI Language feature designed to automatically identify and return the main topics, concepts, and recurring themes from unstructured text, such as 'product quality' or 'shipping delay'. Unlike custom-trained models, this feature requires no training data and works out-of-the-box, making it ideal for quickly summarizing large collections of customer feedback emails.
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
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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 hotel chain wants to automatically analyze guest reviews to identify the most frequently mentioned aspects of their stay, such as 'cleanliness', 'staff friendliness', or 'location'. They want to use a prebuilt Azure AI Language feature without custom training. Which feature should they use?
medium
A.Sentiment Analysis
✓ B.Key Phrase Extraction
C.Entity Recognition
D.Language Detection
Why B: Key Phrase Extraction is the correct choice because it is a prebuilt Azure AI Language feature designed to automatically identify and return the most salient words or phrases from unstructured text, such as 'cleanliness', 'staff friendliness', or 'location'. This feature requires no custom training and directly addresses the hotel chain's need to surface frequently mentioned aspects of guest reviews.
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Question Discussion
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