Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A hotel chain wants to automatically determine whether online guest reviews express a positive, negative, or neutral opinion about their stays. Which built-in Azure AI Language feature should they use?
⚠ Common exam trap
Watch out — candidates often confuse key phrase extraction with sentiment analysis, thinking that extracting phrases like 'bad service' implies sentiment, but key phrase extraction only identifies topics without evaluating their emotional polarity.
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
✓
Sentiment analysis
Sentiment analysis is the correct Azure AI Language feature because it is specifically designed to classify text as positive, negative, or neutral, which directly matches the hotel chain's requirement to determine guest opinions from online reviews. This feature uses machine learning models to evaluate the overall sentiment expressed in a document or sentence, providing a confidence score for each sentiment category.
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
Named Entity Recognition (NER) is designed to extract and classify entities such as person names, organizations, locations, dates, and quantities from text. For hotel reviews, NER might identify the hotel name or the city mentioned, but it does not evaluate the emotional polarity of the surrounding language. Because the requirement is to determine whether reviews are positive or negative, NER provides none of the sentiment signal needed and is therefore incorrect.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies the most important topics, keywords, or short phrases in a document based on statistical salience and context. It might surface terms like 'clean rooms' or 'slow check-in,' but it does not label those phrases as favorable or unfavorable. Without assigning a polarity score or category, key phrase extraction cannot answer whether a review is positive or negative, so it does not fulfill the hotel chain's need.
- ✓
Sentiment analysis
Why this is correct
Sentiment analysis is the direct NLP capability for determining whether text expresses positive, negative, or neutral sentiment. Azure's Text Analytics sentiment analysis returns confidence scores (0 to 1) for each sentiment class at the document or sentence level, allowing automated detection of opinion polarity in online reviews. Since the hotel chain wants to automatically classify reviews as positive or negative, this method precisely matches the requirement, making it the correct answer.
- ✗
Language detection
Why it's wrong here
Language detection identifies the natural language in which a text is written, such as English, Spanish, or French, and returns a language code like 'en' or 'es'. While knowing the language is often a useful preprocessing step in an NLP pipeline, it reveals nothing about the sentiment or emotional content of the review. Therefore, language detection alone cannot determine whether a hotel review is positive or negative, and it is not the correct service for this task.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Confidence score
A confidence score is a number (often between 0 and 1 or 0 and 100%) that tells you how likely it is that an AI model's prediction or answer is correct.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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