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AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure

A hotel chain receives thousands of online guest reviews each month. The management wants to automatically determine whether the overall feedback for each review is positive, negative, or neutral to identify areas for improvement. Which Azure AI Language feature should they use?

⚠ Common exam trap

Many candidates confuse sentiment analysis with key phrase extraction, mistakenly thinking that extracting positive or negative phrases is equivalent to determining overall sentiment, but key phrase extraction does not assign a polarity score or classify the text as positive, negative, or neutral.

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 evaluates text to determine the overall sentiment—positive, negative, or neutral—at the document or sentence level. This directly matches the hotel chain's requirement to classify each review's feedback automatically, enabling them to identify areas for improvement based on sentiment trends.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Key phrase extraction

    Why it's wrong here

    Key phrase extraction is an Azure AI Language feature that returns the most salient phrases, such as 'comfortable beds' or 'delayed check-in', based on linguistic patterns. It does not evaluate the polarity of that content, so it cannot determine whether a review expresses approval or criticism. Sentiment scoring requires a classification model, not just a relevance-based extraction of topics.

  • Named entity recognition

    Why it's wrong here

    Named entity recognition (NER) categorizes spans in text into predefined types like Person, Organization, Location, Date, and Quantity. While it can identify mentions of a hotel's name or amenities, it assigns no emotional valence to those entities. Consequently, NER would miss the overall opinion in a review, making it unsuitable for measuring guest satisfaction across thousands of reviews.

  • Sentiment analysis

    Why this is correct

    Sentiment analysis is the Azure AI Language capability that detects the emotional tone of text and returns both a document-level label (positive, neutral, or negative) and confidence scores for each class. In the guest review scenario, it directly classifies whether feedback is favorable or unfavorable, enabling the hotel chain to aggregate customer satisfaction metrics. This is precisely the capability required by the question stem.

  • Language detection

    Why it's wrong here

    Language detection determines the predominant language of a text (e.g., English, French, or Mandarin) by analyzing character patterns and vocabulary. It does not examine the meaning or sentiment of the content, so a short review like 'terrible' and a long review like 'fantastic stay' would both be detected as English. Thus, language detection answers a different question than the one about guest sentiment.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

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