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 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?
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 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.
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.
✗
Sentiment Analysis
Why it's wrong here
Sentiment Analysis determines the overall sentiment (positive, negative, neutral) but does not extract specific topics.
✓
Key Phrase Extraction
Why this is correct
Correct. This feature extracts main points and topics from text, such as specific aspects mentioned in reviews.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity Recognition
Why it's wrong here
Entity Recognition identifies named entities like people, places, and organizations, not general topics.
✗
Language Detection
Why it's wrong here
Language Detection identifies the language of the text, not its content or themes.
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 Recognition, mistakenly thinking that extracting 'aspects' like 'cleanliness' is a form of entity recognition, when in fact Entity Recognition is limited to predefined categories like Person, Location, or Organization.
Detailed technical explanation
How to think about this question
Key Phrase Extraction uses a statistical natural language processing model that analyzes text structure and term frequency-inverse document frequency (TF-IDF) to rank phrases by relevance. It returns a list of strings, each representing a key concept, and is optimized for extracting multi-word phrases (e.g., 'staff friendliness') as well as single words. In a real-world scenario, a hotel chain could batch-process thousands of reviews and aggregate the extracted phrases to generate a word cloud or frequency chart of guest 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 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.
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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