Describe features of Natural Language Processing workloads on Azure →easyMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
What does sentiment analysis do?
⚠ Common exam trap
Candidates often confuse sentiment analysis with key phrase extraction or entity recognition, because all three are part of the same Text Analytics API, but each serves a distinct purpose—sentiment analysis focuses on emotional tone, not on identifying specific terms or names.
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
✓
Determines the emotional tone (positive, negative, neutral) expressed in text
Sentiment analysis is a natural language processing (NLP) technique that evaluates text to determine the emotional tone expressed, typically classifying it as positive, negative, or neutral. In Azure Cognitive Services, this is performed by the Text Analytics API, which uses machine learning models to assign sentiment scores and labels based on the overall context of the input text. Option B is correct because it directly describes this core function of detecting emotional polarity.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Translates text from one language to another
Why it's wrong here
Translating text into another language is a machine translation task—handled in Azure by Translator—that remaps vocabulary and syntax between language pairs. Sentiment analysis, by contrast, makes no cross-language conversion; it inspects the given text and judges whether its expressed attitude is positive, negative, or neutral. The two operations have different inputs, outputs, and evaluation metrics.
- ✓
Determines the emotional tone (positive, negative, neutral) expressed in text
Why this is correct
Sentiment analysis is a text classification technique that evaluates a piece of text and assigns an emotional polarity label—typically positive, negative, neutral, or mixed—based on the author's opinion. Azure AI Language runs this analysis at both sentence and document levels and can return confidence scores that indicate how strongly the emotion is expressed. It does not translate, extract entities, or transcribe audio; its exclusive output is the polarity and intensity of the sentiment conveyed.
- ✗
Extracts key phrases and named entities from text
Why it's wrong here
Key phrase extraction returns the most important terms or main talking points, while named entity recognition labels specific mentions such as people, organizations, and locations—both are distinct Azure AI Language capabilities. Sentiment analysis, in contrast, does not identify individual terms or entities; it aggregates the entire text's language and assigns an overall positive, negative, or neutral orientation. Mixing these two would confuse a classification output (polarity) with an information-retrieval output (phrases and entities).
- ✗
Converts spoken words into written text
Why it's wrong here
Speech-to-text, offered by Azure Speech, converts audio signals into written words by transcribing the acoustic and language content. Sentiment analysis operates on already transcribed or typed text—it reads the text's emotional tone, not raw audio. So while transcription might feed into sentiment analysis as a preprocessing step, the task of determining the polarity is not a speech-recognition task.
Go deeper
Related to this question
Learn chapter
Machine Learning Core Concepts
Key term
Natural language processing
Natural language processing (NLP) is a branch of artificial intelligence that helps computers understand, interpret, and respond to human language in a way that is both meaningful and useful.
Key term
Machine learning
Machine learning is a branch of artificial intelligence where computers learn patterns from data to make decisions or predictions without being explicitly programmed for every task.
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