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

Answer analysis

Option-by-option breakdown

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

• ✗

  The ability to write Azure AI Language SDK code in multiple programming languages

  Why it's wrong here

  The phrase 'multi-language' in Azure AI Language documentation refers to the number of human languages the NLP models can process, not the programming languages used to write client code. Azure offers SDKs in C#, Python, JavaScript, Java, and Go, but that is a developer experience feature that applies to every Azure service, not a distinguishing capability of the AI Language service. An application written entirely in Python can still analyze text in over 100 human languages, so SDK language support is irrelevant to this question.

• ✓

  NLP capabilities (sentiment, NER, etc.) that work across 100+ human languages for global applications

  Why this is correct

  Azure AI Language provides a single set of NLP APIs—such as sentiment analysis, named entity recognition, and key phrase extraction—that work across more than 100 human languages. This lets a global application send text in English, Spanish, Arabic, Chinese, or many other languages to the same endpoint and receive consistent analysis without building separate pipelines per language. The service automatically detects the input's language and applies the appropriate model, enabling truly multinational scenarios like call-center sentiment tracking or multilingual document review.

• ✗

  Translating all NLP model outputs into the user's preferred language automatically

  Why it's wrong here

  Native multi-language support in Azure AI Language means sentiment, NER, and key phrase extraction operate directly on the original text, returning results in the same language's script and structure. Translating outputs into the user's preferred language would require a separate call to Azure AI Translator and would not change the fact that the underlying NLP analysis recognized the source language. Since translation can introduce errors and lose cultural or contextual nuance, it is not how the Azure AI Language service advertises multi-language support.

• ✗

  Combining multiple NLP models that each specialise in a different language

  Why it's wrong here

  Although Azure AI Language may internally use language-specific or multilingual transformer models, the service exposes a single unified API that automatically detects and processes text in the supported language. A developer does not need to combine per-language specialist models or route requests to different endpoints based on the input's language. Multi-language support here is about the API's ability to accept and analyze many human languages, not about assembling multiple models together in code.

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