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 company receives customer support tickets in over 60 languages. They need to automatically detect the language of each ticket so it can be routed to the appropriate language-specific team. The company has no labeled training data for language identification. Which Azure AI Language feature should they use?
⚠ Common exam trap
The trap here is that candidates might confuse Language Detection with Custom Text Classification, assuming they need to train a model for a multilingual scenario, when in fact Azure provides a built-in, no-code language detection API that requires zero training data.
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 choice because it is a pre-built, zero-shot Azure AI Language feature that can automatically identify the language of text without requiring any labeled training data. The service uses a multilingual model trained on large datasets to detect over 100 languages, making it ideal for routing support tickets in over 60 languages with no prior customization.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Custom Text Classification
Why it's wrong here
Custom Text Classification is a custom model capability in Azure AI Language that requires users to provide labeled documents and train a model to assign user-defined labels/categories (e.g., issue type or department). It cannot infer the language of the input text because it relies on supervised training over your specific classes, and providing no language labels means it has no mechanism to output a detected language.
- ✓
Language Detection
Why this is correct
Language Detection is a prebuilt Azure AI Language service capability that automatically identifies the dominant language and optionally returns a confidence score and ISO 639-1 code for a given text. Because it is prebuilt, it requires no labeled training data or custom model training and supports dozens of languages, making it the correct fit for routing support tickets written in 60+ languages.
- ✗
Key Phrase Extraction
Why it's wrong here
Key Phrase Extraction is a prebuilt text analytics feature that scans a document and surfaces the most relevant nouns/phrases, such as 'refund' or 'login issue', to summarize content. It assumes the language is already known or can be passed as a parameter, and its output is a list of phrases rather than a language identifier, so it cannot tell a support agent which language a ticket is written in.
- ✗
Entity Recognition
Why it's wrong here
Entity Recognition (specifically Named Entity Recognition, NER) detects and categorizes entities in text into predefined types such as Person, Organization, Location, DateTime, and Quantity, and it can link entities to a knowledge base. While NER models are language-specific and may behave differently across languages, their purpose is to extract structured entities from text, not to output the document's language, so selecting it would not solve the multilingual routing problem.
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
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.
Key term
Training data
Training data is a set of examples used to teach a machine learning model how to make predictions or decisions.
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Senior Network & Security Engineer · founder of Courseiva
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