Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
A global news organization receives articles in multiple languages. They need to first identify the language of each article, then translate it into English. Which combination of prebuilt Azure AI services should they use?
⚠ Common exam trap
Candidates often assume the Translator service alone can handle both language detection and translation, but the question explicitly requires two separate services, and the Translator's built-in detection is not a standalone prebuilt service for the first step.
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
✓
Language Detection (Azure AI Language) and Translator (Azure AI Translator)
The scenario requires two distinct steps: first, identifying the language of each article, which is performed by the Language Detection API in Azure AI Language; second, translating the article into English, which is handled by the Azure AI Translator service. These two prebuilt services are designed to work together seamlessly for multilingual content processing.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Language Detection (Azure AI Language) and Translator (Azure AI Translator)
Why this is correct
Language Detection (Azure AI Language) first classifies the dominant language of each incoming article by analyzing character patterns and script statistics, producing a language code such as 'en' or 'fr'. Translator (Azure AI Translator) then uses that detected language as the source to convert the text into the organization's target language, preserving meaning across the pipeline. Together they form the correct end-to-end workflow: detect first, then translate, which matches the requirement exactly.
- ✗
Key Phrase Extraction and Sentiment Analysis
Why it's wrong here
Key Phrase Extraction identifies the main topics or talking points in a document, while Sentiment Analysis returns a sentiment score (positive, negative, neutral, or mixed) for the text. Neither service produces a language code, and neither performs any text conversion between languages; they are both analysis-oriented capabilities, not translation-oriented ones. Thus, this combination completely misses both halves of the stated requirement and would leave articles untranslated.
When this WOULD be correct
A company wants to analyze customer feedback in English to extract key topics and determine overall sentiment. In that scenario, Key Phrase Extraction and Sentiment Analysis would be the correct combination.
- ✗
Entity Recognition and Translator
Why it's wrong here
Entity Recognition (part of Azure AI Language) extracts named entities like organizations, locations, and people, but it never outputs a language identifier or source-language code. Translator could then translate the text, but without knowing which language the source text is in, the translation request lacks a required source parameter and will rely on auto-detection anyway. This pairing is incomplete because Entity Recognition adds no value to the translation process and cannot satisfy the language-identification requirement.
When this WOULD be correct
An exam question asks: 'A company needs to extract names of people and organizations from customer feedback and then translate the feedback into English.' Here, Entity Recognition and Translator would be correct.
- ✗
Language Detection and Text Analytics for Health
Why it's wrong here
Language Detection correctly identifies the source language, but Text Analytics for Health is a domain-specific service designed to extract medical entities, relations, and assertions from unstructured clinical text such as doctor's notes or discharge summaries. It has no translation capability and would return meaningless or irrelevant results if applied to general news articles. This pairing only covers the detection half of the requirement and fails to produce translated output in the target language.
When this WOULD be correct
A healthcare organization needs to analyze multilingual patient records: first detect the language of each record, then extract medical entities and relationships using Text Analytics for Health. In that scenario, Language Detection and Text Analytics for Health would be the correct combination.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓Language Detection (Azure AI Language) and Translator (Azure AI Translator)Correct answer▾
Why this is correct
Language Detection (Azure AI Language) first classifies the dominant language of each incoming article by analyzing character patterns and script statistics, producing a language code such as 'en' or 'fr'. Translator (Azure AI Translator) then uses that detected language as the source to convert the text into the organization's target language, preserving meaning across the pipeline. Together they form the correct end-to-end workflow: detect first, then translate, which matches the requirement exactly.
✗Key Phrase Extraction and Sentiment AnalysisWrong answer — click to see why▾
Why this is wrong here
Key Phrase Extraction and Sentiment Analysis do not identify the language of text or translate it, so they cannot fulfill the requirement of first detecting the language and then translating articles into English.
★ When this WOULD be the correct answer
A company wants to analyze customer feedback in English to extract key topics and determine overall sentiment. In that scenario, Key Phrase Extraction and Sentiment Analysis would be the correct combination.
Why candidates choose this
Candidates may confuse text analytics capabilities with language detection and translation, assuming that Key Phrase Extraction can identify language or that Sentiment Analysis involves translation.
✗Entity Recognition and TranslatorWrong answer — click to see why▾
Why this is wrong here
Entity Recognition identifies entities like people and places but does not perform translation; the question requires both language detection and translation, which Entity Recognition does not provide.
★ When this WOULD be the correct answer
An exam question asks: 'A company needs to extract names of people and organizations from customer feedback and then translate the feedback into English.' Here, Entity Recognition and Translator would be correct.
Why candidates choose this
Candidates may think Entity Recognition is needed to identify content before translation, or they confuse it with language detection, not realizing the specific requirement for language identification.
✗Language Detection and Text Analytics for HealthWrong answer — click to see why▾
Why this is wrong here
Text Analytics for Health is specialized for medical information extraction, not general translation. The question requires translating articles into English, which Translator provides, but Language Detection paired with Text Analytics for Health does not fulfill the translation need.
★ When this WOULD be the correct answer
A healthcare organization needs to analyze multilingual patient records: first detect the language of each record, then extract medical entities and relationships using Text Analytics for Health. In that scenario, Language Detection and Text Analytics for Health would be the correct combination.
Why candidates choose this
Candidates may see 'Language Detection' and assume it pairs with any text analytics service, overlooking that the question explicitly requires translation, which only Translator provides.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Language detection
Language detection is an Azure AI service that automatically identifies the language of a piece of text without manual input.
Key term
Azure AI Language
Azure AI Language is a cloud-based service from Microsoft that uses natural language processing to understand, analyze, and generate human language for applications.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.