Where This Topic Fits in the Exam
- Exam area: Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)
- Sub-area: Identify features of common NLP workload scenarios
- Skill focus: Recognizing when translation is the appropriate NLP workload, and understanding Azure services that support it
Translation is a core NLP workload on the AI-900 exam and often appears in short, scenario-based questions.
What Is Translation in NLP?
Translation is the process of converting text (or speech) from one language into another while preserving the original meaning.
Modern AI-powered translation systems use machine learning and deep learning models to understand context, grammar, and semantics rather than performing word-for-word substitutions.
Key Features of Translation Workloads
Translation solutions typically provide the following features:
- Text-to-text translation between languages
- Support for dozens of languages and dialects
- Context-aware translation (not literal word replacement)
- Detection of source language
- Batch or real-time translation
- Integration with applications, websites, and chatbots
- Optional customization for domain-specific terminology
Common Uses of Translation
Translation workloads are used whenever language differences create a communication barrier.
Typical scenarios include:
- Translating websites or product documentation
- Supporting multilingual customer service
- Translating chat messages in real time
- Localizing applications for global users
- Translating social media posts or reviews
- Enabling communication across international teams
Azure Services for Translation
In Azure, translation capabilities are provided by:
Azure AI Translator
Azure AI Translator is part of Azure AI Services and offers:
- Text translation between supported languages
- Language detection
- Transliteration (converting text between scripts)
- Dictionary lookup and examples
- Real-time and batch translation via APIs
This service uses prebuilt models, so no training is required.
Translation vs Other NLP Workloads
It is important to distinguish translation from similar NLP tasks:
| NLP Task | Purpose |
|---|---|
| Translation | Convert text from one language to another |
| Language detection | Identify which language text is written in |
| Speech recognition | Convert spoken audio into text |
| Speech synthesis | Convert text into spoken audio |
| Sentiment analysis | Identify emotional tone of text |
Translation and Speech
Translation workloads may involve:
- Text-to-text translation (most common on AI-900)
- Speech translation, which combines:
- Speech recognition
- Translation
- Speech synthesis
On the exam, focus primarily on text translation scenarios, unless speech is explicitly mentioned.
Responsible AI Considerations
Translation systems should be designed with responsible AI principles in mind:
- Fairness: Avoid biased or culturally inappropriate translations
- Reliability: Handle idioms and context accurately
- Transparency: Clearly indicate when content is machine-translated
- Privacy: Protect sensitive or personal information in translated text
Exam Clues to Watch For
On AI-900, translation workloads are commonly signaled by phrases such as:
- “Convert content from one language to another”
- “Support multilingual users”
- “Translate customer messages”
- “Localize an application”
When these appear, translation is the correct NLP workload.
Key Takeaways for AI-900
- Translation is an NLP workload that converts text between languages
- Azure AI Translator is the primary Azure service for translation
- No model training is required
- Translation is different from sentiment analysis, entity recognition, and speech workloads
- Exam questions are typically scenario-based and concise
Go to the Practice Exam Questions for this topic.
Go to the AI-900 Exam Prep Hub main page.
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