NLP & Azure Language — AI-900 Practice Question
A representative Microsoft Azure AI Fundamentals (AI-900) exam question on NLP & Azure Language. Work through it below, then read why each option is right or wrong.
Short answer
The correct answer is C. Sentiment Analysis.
Sentiment Analysis determines whether text expresses positive, negative, or neutral sentiment, making it ideal for understanding customer satisfaction from reviews. It can identify reviews with negative sentiment that may require immediate attention. Named Entity Recognition (A) identifies entities like names and places. Key Phrase Extraction (B) identifies important terms. Language Detection (D) identifies what language text is written in.
The Question
A hotel chain wants to automatically analyze guest reviews to understand overall customer satisfaction and identify reviews that require immediate attention. Which NLP capability should they use?
Why C is correct
Sentiment Analysis determines whether text expresses positive, negative, or neutral sentiment, making it ideal for understanding customer satisfaction from reviews. It can identify reviews with negative sentiment that may require immediate attention. Named Entity Recognition (A) identifies entities like names and places. Key Phrase Extraction (B) identifies important terms. Language Detection (D) identifies what language text is written in.
Why the other options are wrong
Option A does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option B does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Option D does not satisfy the requirement in the scenario. Review the explanation above: the correct choice (C) is the only one that fully meets every constraint stated in the question.
Key idea: NLP & Azure Language
Sentiment Analysis determines whether text expresses positive, negative, or neutral sentiment, making it ideal for understanding customer satisfaction from reviews. It can identify reviews with negative sentiment that may require immediate attention. Named Entity Recognition (A) identifies entities like names and places. Key Phrase Extraction (B) identifies important terms. Language Detection (D) identifies what language text is written in. On the AI-900 exam, questions in the "Natural Language Processing on Azure" domain test whether you can map a scenario's constraints to the right choice. Read the requirement carefully, eliminate options that violate any single constraint, and pick the one that satisfies all of them with the least operational overhead.
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