The answer is Azure Translator. This service is the correct choice because it specializes in text-to-text translation across over 100 languages, using a REST API that can handle both real-time and batch processing of written content, which perfectly matches the need to convert multilingual customer feedback emails into English for centralized analysis. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Azure’s AI services: Azure Translator handles text translation, while Azure Speech translates spoken language, and Azure Language Service focuses on sentiment or key phrase extraction—a common trap is confusing these. A helpful memory tip is to think of “Translator” for text on a page, and “Speech” for words out loud.
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.
A multinational corporation receives customer feedback emails in several languages. The company wants to translate all emails into English for centralized analysis by its support team. Which Azure service should they use?
Why wrong: Azure AI Language provides features like sentiment analysis, key phrase extraction, and language detection, but it does not include text translation.
B
Azure Translator
Azure Translator is a dedicated cloud service for text translation between multiple languages, making it the correct choice for this scenario.
C
Azure OpenAI Service
Why wrong: While Azure OpenAI can perform translation via prompts, it is not a specialized translation service and may be less accurate and more costly than Azure Translator.
D
Azure Speech Service
Why wrong: Azure Speech Service is designed for speech-to-text, text-to-speech, and speech translation, not for translating written text input.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Translator
Azure Translator is the correct service because it is specifically designed for text-to-text translation across multiple languages, making it ideal for translating customer feedback emails into English. It provides a REST API that supports real-time or batch translation of text, which aligns with the requirement to process emails in various languages for centralized analysis.
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.
✗
Azure AI Language
Why it's wrong here
Azure AI Language provides features like sentiment analysis, key phrase extraction, and language detection, but it does not include text translation.
✓
Azure Translator
Why this is correct
Azure Translator is a dedicated cloud service for text translation between multiple languages, making it the correct choice for this scenario.
Related concept
Read the scenario before looking for a memorised answer.
✗
Azure OpenAI Service
Why it's wrong here
While Azure OpenAI can perform translation via prompts, it is not a specialized translation service and may be less accurate and more costly than Azure Translator.
✗
Azure Speech Service
Why it's wrong here
Azure Speech Service is designed for speech-to-text, text-to-speech, and speech translation, not for translating written text input.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Azure AI Language's language detection or text analysis features with translation, but Azure AI Language does not include translation functionality, which is exclusively handled by Azure Translator.
Trap categories for this question
Keyword trap
Azure AI Language provides features like sentiment analysis, key phrase extraction, and language detection, but it does not include text translation.
Detailed technical explanation
How to think about this question
Azure Translator uses neural machine translation (NMT) models that analyze the entire context of a sentence to produce more accurate and fluent translations compared to older statistical methods. It supports over 100 languages and can be integrated via a simple HTTP POST request to the 'https://api.cognitive.microsofttranslator.com/translate' endpoint, with parameters for source and target languages. In a real-world scenario, the company could batch-process emails using the Translator's document translation feature, which preserves formatting and handles large volumes efficiently.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: Azure Translator — Azure Translator is the correct service because it is specifically designed for text-to-text translation across multiple languages, making it ideal for translating customer feedback emails into English. It provides a REST API that supports real-time or batch translation of text, which aligns with the requirement to process emails in various languages for centralized analysis.
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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Variation 1. A global customer support team receives feedback messages in multiple languages. They want to build an automated pipeline that first identifies the language of each message, then translates it to English, and finally analyzes the sentiment of the translated text. Which combination of Azure AI services should they use?
hard
A.Azure AI Translator and Azure Anomaly Detector
✓ B.Azure AI Translator and Azure AI Language (Text Analytics)
C.Azure AI Speech and Azure AI Language (Text Analytics)
D.Azure AI Vision and Azure AI Language (Text Analytics)
Why B: Option B is correct because the pipeline requires language detection, translation, and sentiment analysis. Azure AI Translator provides language detection and translation, while Azure AI Language (Text Analytics) provides sentiment analysis on the translated English text. This combination directly fulfills all three requirements.
Last reviewed: Jun 11, 2026
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