- A
Key phrase extraction and sentiment analysis
Key phrase extraction surfaces important topics from text, and sentiment analysis provides emotional tone. Together, they allow the team to identify common complaint themes and track sentiment shifts without any training.
- B
Entity recognition and language detection
Why wrong: Entity recognition finds named entities (e.g., people, organizations), which may not capture general complaint reasons. Language detection identifies the language used, which is unlikely to be the primary need. This combination does not directly address complaint reasons or sentiment.
- C
Text summarization and question answering
Why wrong: Text summarization creates a concise overview of a conversation, but it does not explicitly extract multiple common reasons across many logs. Question answering provides answers to specific queries from a knowledge base, not suitable for open-ended analysis of complaints.
- D
Conversational language understanding and personal identification
Why wrong: Conversational language understanding (CLU) is a custom feature that requires training on intents and entities. Personal identification extracts PII, which is irrelevant to extracting complaint reasons or sentiment. This combination is overkill and not appropriate for prebuilt analysis.
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 support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?
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
Key phrase extraction and sentiment analysis
Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.
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.
- ✓
Key phrase extraction and sentiment analysis
Why this is correct
Key phrase extraction surfaces important topics from text, and sentiment analysis provides emotional tone. Together, they allow the team to identify common complaint themes and track sentiment shifts without any training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity recognition and language detection
Why it's wrong here
Entity recognition finds named entities (e.g., people, organizations), which may not capture general complaint reasons. Language detection identifies the language used, which is unlikely to be the primary need. This combination does not directly address complaint reasons or sentiment.
- ✗
Text summarization and question answering
Why it's wrong here
Text summarization creates a concise overview of a conversation, but it does not explicitly extract multiple common reasons across many logs. Question answering provides answers to specific queries from a knowledge base, not suitable for open-ended analysis of complaints.
- ✗
Conversational language understanding and personal identification
Why it's wrong here
Conversational language understanding (CLU) is a custom feature that requires training on intents and entities. Personal identification extracts PII, which is irrelevant to extracting complaint reasons or sentiment. This combination is overkill and not appropriate for prebuilt analysis.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse entity recognition (which finds specific names or dates) with key phrase extraction (which finds general topics), or think conversational language understanding is needed when prebuilt features suffice for the stated requirements.
Detailed technical explanation
How to think about this question
Key phrase extraction uses a statistical model to return a list of key terms that represent the main points of the text, which is ideal for aggregating common complaint topics across many logs. Sentiment analysis provides a continuous score from 0 to 1 for positive, negative, and neutral sentiment, and can be applied at the sentence level to track how sentiment evolves turn-by-turn in a conversation. In a real-world scenario, the team could use the Azure AI Language REST API to batch-process chat logs, extracting key phrases to build a frequency table of complaint reasons and plotting sentiment scores over time to identify escalation points.
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: Key phrase extraction and sentiment analysis — Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.
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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