The correct answer is combining sentiment analysis and key phrase extraction. Sentiment analysis evaluates the overall tone of each review as positive, negative, or neutral by assigning a numerical score, while key phrase extraction identifies the most frequently mentioned features like “room cleanliness” or “staff friendliness.” Together, these two Azure AI Language features directly address the hotel chain’s need to gauge feedback tone and extract common topics from thousands of reviews. On the AI-900 exam, this scenario tests your understanding of how prebuilt sentiment analysis and key phrase extraction for hotel reviews solve real-world text analytics problems—a common trap is confusing key phrase extraction with entity linking, which identifies named entities like people or places rather than descriptive phrases. Remember the memory tip: “Sentiment scores the vibe; key phrases grab the vibe’s details.”
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 analyze thousands of guest reviews to understand the overall tone of feedback (positive or negative) and to extract the most commonly mentioned features (e.g., 'room cleanliness', 'staff friendliness', 'breakfast'). Which two Azure AI Language features should they combine?
Sentiment analysis gives overall tone, and key phrase extraction pulls out frequent terms like 'staff friendliness', together answering both needs.
B
Entity recognition and language detection
Why wrong: Entity recognition extracts named entities (e.g., hotel names), not general features; language detection only tells the language, not sentiment or topics.
C
Key phrase extraction and entity linking
Entity linking disambiguates entities to knowledge bases, but does not provide sentiment analysis.
D
Text summarization and sentiment analysis
Why wrong: Summarization produces a condensed version, but does not extract specific key phrases or features; sentiment analysis alone does not give the list of features.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Sentiment analysis and key phrase extraction
The hotel chain needs to determine the overall tone (positive/negative) of guest reviews and extract commonly mentioned features. Sentiment analysis provides the overall tone by assigning a sentiment score to each review, while key phrase extraction identifies the most salient phrases such as 'room cleanliness' and 'staff friendliness'. Combining these two Azure AI Language features directly addresses both requirements.
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 and key phrase extraction
Why this is correct
Sentiment analysis gives overall tone, and key phrase extraction pulls out frequent terms like 'staff friendliness', together answering both needs.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity recognition and language detection
Why it's wrong here
Entity recognition extracts named entities (e.g., hotel names), not general features; language detection only tells the language, not sentiment or topics.
✓
Key phrase extraction and entity linking
Why this is correct
Entity linking disambiguates entities to knowledge bases, but does not provide sentiment analysis.
Related concept
Read the scenario before looking for a memorised answer.
✗
Text summarization and sentiment analysis
Why it's wrong here
Summarization produces a condensed version, but does not extract specific key phrases or features; sentiment analysis alone does not give the list of features.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse key phrase extraction with entity recognition or entity linking, thinking that extracting features requires named entity recognition, when in fact key phrase extraction is designed to pull out multi-word phrases like 'room cleanliness' that are not necessarily named entities.
Trap categories for this question
Keyword trap
Summarization produces a condensed version, but does not extract specific key phrases or features; sentiment analysis alone does not give the list of features.
Detailed technical explanation
How to think about this question
Sentiment analysis in Azure AI Language uses machine learning classifiers to assign a sentiment label (positive, negative, neutral, or mixed) and confidence scores at the document and sentence level. Key phrase extraction employs a statistical model that identifies the most important words and phrases by analyzing term frequency and context, often using a TF-IDF-like approach. In practice, combining these allows the hotel chain to not only gauge overall satisfaction but also pinpoint which aspects of the stay drive positive or negative sentiment, enabling targeted improvements.
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: Sentiment analysis and key phrase extraction — The hotel chain needs to determine the overall tone (positive/negative) of guest reviews and extract commonly mentioned features. Sentiment analysis provides the overall tone by assigning a sentiment score to each review, while key phrase extraction identifies the most salient phrases such as 'room cleanliness' and 'staff friendliness'. Combining these two Azure AI Language features directly addresses both requirements.
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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