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 global social media platform wants to automatically detect the language of user posts to route them to appropriate content moderators. The posts are short and often contain mixed scripts. Which Azure AI Language feature should they use?
⚠ Common exam trap
Candidates often confuse language detection with sentiment analysis or key phrase extraction, assuming any NLP feature can identify language, but only Language Detection is purpose-built for this task.
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
✓
Language detection
Language detection is the correct Azure AI Language feature because it is specifically designed to identify the primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.
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
Why it's wrong here
Sentiment analysis evaluates the emotional tone of text—positive, negative, or neutral—by examining lexical polarity and context. It does not return a language code or confidence score, so an enthusiastic English post and an equally enthusiastic Spanish post would both be classified simply as 'positive'. Because it ignores the actual language of the input, it cannot be used to route posts to language-specific moderators.
- ✓
Language detection
Why this is correct
Azure AI Language's language detection (also called language identification) examines the text and returns the dominant language (e.g., 'en', 'es', 'fr') alongside a numeric confidence score from 0 to 1. It can even process documents with multiple languages by evaluating individual text segments. This is precisely the capability needed to automatically determine the language of a post before routing it to the appropriate language-specific moderation queue.
- ✗
Key phrase extraction
Why it's wrong here
Key phrase extraction scans text and returns the most prominent words and phrases, often used for tagging or summarization. It works by analyzing part-of-speech patterns and term importance, but it never outputs an ISO language code or confidence value. Even when the extracted phrases are culturally distinctive, the service provides no metadata indicating the language of the source text, so it cannot fulfill the detection requirement.
- ✗
Entity recognition
Why it's wrong here
Entity recognition, also known as Named Entity Recognition (NER), identifies and categorizes spans of text as people, organizations, locations, dates, or other predefined types. Its output is a set of entities with labels and offsets, not a language identifier. Although certain entity names may seem language-specific, the same entity name can legitimately appear across many languages, so this method cannot reliably determine which language the text itself is written in.
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Azure Machine Learning Studio
Key term
Language detection
Language detection is an Azure AI service that automatically identifies the language of a piece of text without manual input.
Key term
Feature
A feature is a distinct unit of functionality that delivers value to the user, often managed and tracked throughout the software development lifecycle.
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