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
A global e-commerce company receives product reviews in multiple languages. They want to automatically identify the language of each review to route it to the appropriate translation queue. Which Azure AI Language feature should they use?
⚠ Common exam trap
The trap here is that candidates might confuse Language Detection with Sentiment Analysis or Key Phrase Extraction because all three are Natural Language Processing features, but only Language Detection answers the specific question of identifying the language of the text.
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 language of a given text input, returning a language name and a confidence score. This directly meets the requirement to automatically detect the language of product reviews so they can be routed to the appropriate translation queue.
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 in Azure AI Language identifies whether a review is positive, negative, neutral, or mixed by evaluating the emotional tone expressed in the text, producing polarity scores. It does not, however, determine the language of the text, because sentiment models are typically built and run per language. For routing reviews based on language, this feature would be useless—it would try to score sentiment on text whose language is unknown, and the output gives no ISO language code or confidence score.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction returns a ranked list of the main talking points in a review, such as product names, packaging issues, or delivery delays, by applying linguistic analysis to identify salient terms. While it can infer meaning from the text, it has no mechanism for outputting the language identifier or ISO 639-1 code. In the e-commerce workflow, extracting phrases before knowing the language may call the wrong language-specific model, so it does not satisfy the need to route reviews to the correct language pipeline.
- ✓
Language Detection
Why this is correct
Azure AI Language Detection is the correct feature because it is specifically designed to identify the language(s) in which text is written, supporting over 100 languages and returning the language name, ISO code, and confidence score for each document. A review written in French, for example, would yield 'fr' or 'French' with a high confidence score, allowing the e-commerce system to automatically route it to a French-language processor. Because reviews arrive in many languages, this language detection output is the essential first step before applying any language-specific analytics.
- ✗
Entity Recognition
Why it's wrong here
Entity Recognition (NER, named entity recognition) extracts occurrences of known entity types from text, such as person names, organizations, locations, dates, and quantities, tagging them with categories. It does not classify the overall language of the document and offers no language identifier, even though some entity labels or names might hint at a culture. For a system that must route reviews by language, detecting individual entities is irrelevant—the process would miss the fundamental question of which language the review is written in.
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Azure Machine Learning Studio
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
Language detection
Language detection is an Azure AI service that automatically identifies the language of a piece of text without manual input.
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