The answer is language detection, the correct Azure AI Language feature for automatically identifying the primary language of user posts. This feature is specifically designed to analyze text input, including short phrases and mixed-script content, and return a language name and confidence score, making it ideal for routing posts to appropriate content moderators based on language. On the AI-900 exam, this scenario tests your understanding of Azure AI Language’s prebuilt capabilities, often contrasting language detection with sentiment analysis or key phrase extraction—common traps where candidates confuse identifying language with analyzing emotion or topics. Remember, language detection is the first step in multilingual workflows, and the exam emphasizes its ability to handle noisy, short text like social media posts. A simple memory tip: think “Lang Detect” for “Language Direction”—it tells you which language to route content toward.
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 global social media platform wants to automatically detect the language of user posts to route them to appropriate content moderators. The posts are short and often contain mixed scripts. Which Azure AI Language feature should they use?
Why wrong: Sentiment analysis determines whether text expresses positive, negative, or neutral sentiment. It does not identify the language.
B
Language detection
Language detection analyzes text and returns the dominant language along with a confidence score, suitable for routing posts to language-specific moderators.
C
Key phrase extraction
Why wrong: Key phrase extraction identifies important terms and phrases in text, but does not determine the language itself.
D
Entity recognition
Why wrong: Entity recognition identifies and categorizes named entities (e.g., people, locations, organizations) but does not detect the language of the text.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Language detection
Language detection is the correct Azure AI Language feature because it is specifically designed to identify the primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.
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.
✗
Sentiment analysis
Why it's wrong here
Sentiment analysis determines whether text expresses positive, negative, or neutral sentiment. It does not identify the language.
✓
Language detection
Why this is correct
Language detection analyzes text and returns the dominant language along with a confidence score, suitable for routing posts to language-specific moderators.
Related concept
Read the scenario before looking for a memorised answer.
✗
Key phrase extraction
Why it's wrong here
Key phrase extraction identifies important terms and phrases in text, but does not determine the language itself.
✗
Entity recognition
Why it's wrong here
Entity recognition identifies and categorizes named entities (e.g., people, locations, organizations) but does not detect the language of the text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse language detection with sentiment analysis or key phrase extraction, assuming any NLP feature can identify language, but only Language Detection is purpose-built for this task.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important terms and phrases in text, but does not determine the language itself.
Detailed technical explanation
How to think about this question
Azure Language Detection uses a deep neural network model trained on millions of documents across 120+ languages. For short or mixed-script posts, the service employs a confidence score (0–1) and can return multiple languages if ambiguity is detected, with the highest score indicating the primary language. This is critical for social media where posts may contain code-switching or transliterations.
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: Language detection — Language detection is the correct Azure AI Language feature because it is specifically designed to identify the primary language of text, including short and mixed-script content. This allows the platform to automatically route posts to moderators who speak the detected language, directly addressing the requirement.
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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Question Discussion
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