Question 43 of 1,020

Quick Answer

The correct answer is that language detection in Azure AI Language identifies the natural human language of input text and returns a confidence score. This is the right choice because the Azure AI Language service uses pre-trained machine learning models to analyze text and classify it into over 100 languages, outputting a numeric score between 0 and 1 that indicates the probability the detected language is correct. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of a core, pre-built capability of the Language service—one that requires no custom training data from you. A common trap is confusing language detection with sentiment analysis or key phrase extraction; remember that language detection is always the first step, as the service must know the language before applying other analyses. For a quick memory tip, think of the confidence score as the service’s “certainty meter”—the closer to 1.0, the more confident the model is in its guess.

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

This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.

What is 'language detection' in Azure AI Language?

Question 1easymultiple choice
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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

Identifying the natural human language of input text with a confidence score

Language detection in Azure AI Language identifies the natural human language of input text and returns a confidence score indicating the likelihood of the detected language being correct. This is a core capability of the Language service, using pre-trained machine learning models to classify text into over 100 languages without requiring any training data from the user.

Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Answer analysis

Option-by-option breakdown

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

  • Automatically identifying which programming language a code snippet is written in

    Why it's wrong here

    Programming language detection is a code analysis feature — language detection in Azure AI Language identifies natural human languages.

  • Identifying the natural human language of input text with a confidence score

    Why this is correct

    Language detection recognises the language of text (English, French, Arabic, etc.) — enabling automatic routing in multilingual applications.

    Related concept

    Read the scenario before looking for a memorised answer.

  • Checking whether text contains any language that violates community guidelines

    Why it's wrong here

    Policy violation detection is content moderation — language detection identifies which natural language the text is written in.

  • Translating text from any language into the user's preferred language

    Why it's wrong here

    Translation is Azure AI Translator — language detection only identifies the language, it does not translate it.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates often confuse language detection with translation (Option D) or content moderation (Option C), but the exam specifically tests the distinction between identifying a language and performing an action on it.

Detailed technical explanation

How to think about this question

Under the hood, Azure AI Language uses a transformer-based neural network trained on vast multilingual corpora to predict the language of each document. The confidence score ranges from 0 to 1, and the service can also return a list of alternative languages with lower scores if the input is ambiguous. In real-world scenarios, language detection is critical for routing customer support tickets to the correct language-specific queue or for preprocessing text before downstream NLP tasks like sentiment analysis or key phrase extraction.

KKey Concepts to Remember

  • Read the scenario before looking for a memorised answer.
  • Find the constraint that changes the correct option.
  • Eliminate answers that are true in general but not in this case.

TExam Day Tips

  • Watch for words such as best, first, most likely and least administrative effort.
  • Review why wrong options are wrong, not only why the correct option is correct.

Key takeaway

Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.

Real-world example

How this comes up in practice

A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.

What to study next

Got this wrong? Here's your next step.

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FAQ

Questions learners often ask

What does this AI-900 question test?

Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: Identifying the natural human language of input text with a confidence score — Language detection in Azure AI Language identifies the natural human language of input text and returns a confidence score indicating the likelihood of the detected language being correct. This is a core capability of the Language service, using pre-trained machine learning models to classify text into over 100 languages without requiring any training data from the user.

What should I do if I get this AI-900 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

What is the key concept behind this question?

Read the scenario before looking for a memorised answer.

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Same concept, more angles

1 more ways this is tested on AI-900

These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.

Variation 1. What does the Azure AI Translator's language detection feature do when no source language is specified?

easy
  • A.It refuses to translate and returns an error
  • B.It automatically detects the source language and includes it in the translation response
  • C.It defaults to English as the assumed source language
  • D.It translates the text into every supported language

Why B: When no source language is specified in Azure AI Translator, the language detection feature automatically identifies the language of the input text and includes the detected language code in the translation response. This is a core capability of the service, enabling seamless translation without requiring the user to pre-identify the source language.

Last reviewed: Jun 11, 2026

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