Practice Questions

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Question 1

Which Azure service is primarily used to translate text between languages?

A. Azure Speech Service
B. Azure Language Service
C. Azure Translator
D. Azure OpenAI Service

✅ Correct Answer: C. Azure Translator

Explanation:
Azure Translator (part of Azure AI Services) is specifically designed for text translation across multiple languages. While other services handle NLP or speech, Translator focuses on multilingual text conversion.

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Question 2

A company wants to translate product descriptions on a website in real time for international users. Which feature of Azure Translator best supports this scenario?

A. Batch transcription
B. Real-time REST API translation
C. Sentiment analysis
D. Custom question answering

✅ Correct Answer: B. Real-time REST API translation

Explanation:
Azure Translator provides REST APIs that allow applications and websites to translate text dynamically as users access content.

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Question 3

Which scenario is the best example of using machine translation?

A. Detecting the emotional tone of customer feedback
B. Extracting key phrases from documents
C. Translating an email from English to French
D. Identifying people and locations in text

✅ Correct Answer: C. Translating an email from English to French

Explanation:
Machine translation focuses on converting text from one language to another, which is exactly what this scenario describes.

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Question 4

What type of translation does Azure Translator perform by default?

A. Rule-based translation
B. Human-assisted translation
C. Statistical translation
D. Neural machine translation

✅ Correct Answer: D. Neural machine translation

Explanation:
Azure Translator uses Neural Machine Translation (NMT) models, which rely on deep learning to produce more natural and accurate translations.

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Question 5

A travel application needs to detect the source language of user input before translating it. Can Azure Translator support this requirement?

A. No, language detection requires Azure Language Service
B. Yes, language detection is built into Azure Translator
C. Only if custom models are trained
D. Only for speech input

✅ Correct Answer: B. Yes, language detection is built into Azure Translator

Explanation:
Azure Translator can automatically detect the source language of text before translating it, which is a common real-world scenario.

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Question 6

Which of the following is a common use case for translation in Azure?

A. Voice-controlled virtual assistants
B. Multilingual customer support chatbots
C. Facial recognition systems
D. Predictive maintenance systems

✅ Correct Answer: B. Multilingual customer support chatbots

Explanation:
Translation enables chatbots and support systems to communicate with users in multiple languages, improving global accessibility.

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Question 7

A company needs consistent translation for industry-specific terminology (for example, legal or medical terms). What Azure Translator feature helps with this?

A. Language detection
B. Speech synthesis
C. Custom Translator
D. Sentiment scoring

✅ Correct Answer: C. Custom Translator

Explanation:
Custom Translator allows organizations to train translation models using their own terminology, improving accuracy for specialized domains.

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Question 8

Which input format is supported by Azure Translator?

A. Text only
B. Audio only
C. Text and images
D. Text only (speech requires another service)

✅ Correct Answer: D. Text only (speech requires another service)

Explanation:
Azure Translator works with text input. For speech-to-speech translation, Azure Speech Service is used in combination with translation.

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Question 9

Which Azure service would you combine with Azure Translator to build a speech-to-speech translation application?

A. Azure Vision Service
B. Azure Speech Service
C. Azure Language Service
D. Azure Bot Service only

✅ Correct Answer: B. Azure Speech Service

Explanation:
Speech-to-speech translation requires speech recognition (speech-to-text) and speech synthesis (text-to-speech), which are handled by Azure Speech Service, alongside translation.

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Question 10

Why is translation considered a core Natural Language Processing (NLP) workload?

A. It analyzes numerical data patterns
B. It processes and understands human language
C. It detects objects in images
D. It forecasts future values

✅ Correct Answer: B. It processes and understands human language

Explanation:
Translation involves understanding and generating human language, making it a foundational NLP workload alongside sentiment analysis, entity recognition, and language modeling.

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