- A
Sentiment analysis and key phrase extraction
Sentiment analysis provides the emotional tone, and key phrase extraction pulls out the main subjects mentioned, together giving a complete picture of customer feedback.
- B
Language detection and entity extraction
Language detection identifies the language but does not measure sentiment. Entity extraction identifies specific named items (e.g., product names), not general topics or sentiment.
- C
Text summarization and question answering
Why wrong: Summarization condenses text, and question answering extracts answers to specific queries. Neither directly provides sentiment analysis or topic identification.
- D
Named entity recognition and translation
Why wrong: NER finds entities like dates and organizations; translation converts language. Neither addresses sentiment or topic discovery.
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 customer service team wants to analyze chat transcripts to understand customer sentiment and identify the most frequently discussed topics. Which two Azure AI Language features should they combine to achieve this?
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
Sentiment analysis and key phrase extraction
Sentiment analysis evaluates the emotional tone (positive, negative, neutral) of chat transcripts to understand customer sentiment, while key phrase extraction identifies the most frequently discussed topics by pulling out important terms and phrases. Combining these two features directly addresses the requirement to both gauge sentiment and surface recurring topics from the text.
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 this is correct
Sentiment analysis provides the emotional tone, and key phrase extraction pulls out the main subjects mentioned, together giving a complete picture of customer feedback.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Language detection and entity extraction
Why this is correct
Language detection identifies the language but does not measure sentiment. Entity extraction identifies specific named items (e.g., product names), not general topics or sentiment.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Text summarization and question answering
Why it's wrong here
Summarization condenses text, and question answering extracts answers to specific queries. Neither directly provides sentiment analysis or topic identification.
- ✗
Named entity recognition and translation
Why it's wrong here
NER finds entities like dates and organizations; translation converts language. Neither addresses sentiment or topic discovery.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'entity extraction' (which identifies specific named entities like people or places) with 'key phrase extraction' (which identifies general topics and themes), leading them to incorrectly choose option B or D.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's sentiment analysis uses a pre-trained deep learning model that assigns a sentiment score (0 to 1) for each sentence and document, while key phrase extraction leverages a statistical model that identifies noun phrases and important terms based on term frequency and context. In a real-world scenario, a customer service team could use these to automatically flag negative chats about a specific product feature (e.g., 'billing error') for immediate escalation.
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: Sentiment analysis and key phrase extraction — Sentiment analysis evaluates the emotional tone (positive, negative, neutral) of chat transcripts to understand customer sentiment, while key phrase extraction identifies the most frequently discussed topics by pulling out important terms and phrases. Combining these two features directly addresses the requirement to both gauge sentiment and surface recurring topics from the text.
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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Last reviewed: Jun 11, 2026
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