- A
Sentiment analysis and key phrase extraction
Why wrong: Sentiment analysis assesses positive/negative emotion but does not identify the language. Key phrase extraction alone cannot determine the language.
- B
Language detection and entity extraction
Language detection identifies the language, but entity extraction is for specific entities like names, not for summarizing the issue with key phrases.
- C
Language detection and key phrase extraction
Language detection first identifies the language, then key phrase extraction identifies the main points in the email, which together allow routing and summary generation.
- D
Entity extraction and sentiment analysis
Why wrong: Neither of these identifies the language, and entity extraction does not summarize the issue; it extracts specific predefined entities.
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. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 customer support team receives emails in multiple languages. They want to automatically determine the language of each email and then extract key phrases to summarize the issue. Which two Azure AI Language features should they use in sequence?
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 and entity extraction
Option C is correct because the scenario requires first identifying the language of each email (using Language Detection) and then extracting key phrases from the text to summarize the issue (using Key Phrase Extraction). These two features are designed to work sequentially in Azure AI Language, where language detection provides the language code needed for key phrase extraction to operate accurately.
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 and key phrase extraction
Why it's wrong here
Sentiment analysis assesses positive/negative emotion but does not identify the language. Key phrase extraction alone cannot determine the language.
- ✓
Language detection and entity extraction
Why this is correct
Language detection identifies the language, but entity extraction is for specific entities like names, not for summarizing the issue with key phrases.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Language detection and key phrase extraction
Why this is correct
Language detection first identifies the language, then key phrase extraction identifies the main points in the email, which together allow routing and summary generation.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity extraction and sentiment analysis
Why it's wrong here
Neither of these identifies the language, and entity extraction does not summarize the issue; it extracts specific predefined entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'entity extraction' with 'key phrase extraction' or assume sentiment analysis is needed for summarization, when in fact the correct sequence is language detection followed by key phrase extraction to meet the specific workflow of identifying language then summarizing the issue.
Trap categories for this question
Keyword trap
Sentiment analysis assesses positive/negative emotion but does not identify the language. Key phrase extraction alone cannot determine the language.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's language detection uses a deep neural network trained on multilingual corpora to output a language code (e.g., 'en', 'fr') and a confidence score. Key phrase extraction then leverages a TF-IDF-based model combined with linguistic analysis to identify the most salient phrases, and it requires the language parameter to be set correctly for optimal results—if the language is unknown, the service defaults to English, which can degrade accuracy for non-English emails.
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.
What to study next
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FAQ
Questions learners often ask
What does this AI-900 question test?
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 and entity extraction — Option C is correct because the scenario requires first identifying the language of each email (using Language Detection) and then extracting key phrases from the text to summarize the issue (using Key Phrase Extraction). These two features are designed to work sequentially in Azure AI Language, where language detection provides the language code needed for key phrase extraction to operate accurately.
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.
About these practice questions
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Last reviewed: Jun 11, 2026
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.
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