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 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?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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 uses Azure AI Language's prebuilt model to scan chat logs for salient terms and multi-word expressions, isolating recurring complaint topics such as 'refund delay' or 'login error' without requiring custom training. Sentiment analysis scores each text segment on a positive-to-negative continuum, letting the team quantify how customer emotion evolves over time. Combining these prebuilt capabilities directly surfaces both the primary reasons for dissatisfaction (via key phrases) and the strength of negative feeling (via sentiment scores), making this the correct choice for theme discovery and trend tracking.
- ✗
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.
When this WOULD be correct
A scenario where a team needs to generate concise summaries of long customer support conversations and then allow users to ask questions about the content (e.g., 'What was the resolution time?') would make text summarization and question answering the correct combination.
- ✗
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.
When this WOULD be correct
If the question asked for a solution to build a custom chatbot that understands user intents (e.g., 'reset password') and extracts personally identifiable information (PII) from conversations, using CLU and PII detection would be correct, as CLU can be trained on custom data and PII detection is a prebuilt feature.
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.
✓Key phrase extraction and sentiment analysisCorrect answer▾
Why this is correct
Key phrase extraction uses Azure AI Language's prebuilt model to scan chat logs for salient terms and multi-word expressions, isolating recurring complaint topics such as 'refund delay' or 'login error' without requiring custom training. Sentiment analysis scores each text segment on a positive-to-negative continuum, letting the team quantify how customer emotion evolves over time. Combining these prebuilt capabilities directly surfaces both the primary reasons for dissatisfaction (via key phrases) and the strength of negative feeling (via sentiment scores), making this the correct choice for theme discovery and trend tracking.
✗Text summarization and question answeringWrong answer — click to see why▾
Why this is wrong here
The question requires identifying common reasons from chat logs (key phrase extraction) and tracking sentiment changes (sentiment analysis). Text summarization and question answering do not extract key phrases or track sentiment, so they don't meet the stated requirements.
★ When this WOULD be the correct answer
A scenario where a team needs to generate concise summaries of long customer support conversations and then allow users to ask questions about the content (e.g., 'What was the resolution time?') would make text summarization and question answering the correct combination.
Why candidates choose this
Candidates might think summarization can identify common reasons by condensing logs, and question answering could extract specific complaints, but they overlook that the question explicitly asks for identifying common reasons (key phrases) and tracking sentiment, not summarizing or answering questions.
✗Conversational language understanding and personal identificationWrong answer — click to see why▾
Why this is wrong here
Conversational language understanding (CLU) requires custom training to understand intents and entities, and personal identification is not relevant to analyzing common complaint reasons or sentiment changes. The question specifies using prebuilt features without custom training, so CLU is not applicable.
★ When this WOULD be the correct answer
If the question asked for a solution to build a custom chatbot that understands user intents (e.g., 'reset password') and extracts personally identifiable information (PII) from conversations, using CLU and PII detection would be correct, as CLU can be trained on custom data and PII detection is a prebuilt feature.
Why candidates choose this
Candidates may mistakenly think that analyzing chat logs requires understanding the conversation's intent and extracting personal details, but the question focuses on identifying common complaint reasons (key phrases) and tracking sentiment, not on custom intents or PII.
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?”
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Related to this question
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Azure Machine Learning Studio
Key term
Sentiment analysis
Sentiment analysis is a natural language processing technique that uses machine learning to determine the emotional tone or opinion expressed in a piece of text.
Key term
Key phrase extraction
Key phrase extraction is an Azure AI service feature that automatically identifies and extracts the most important words and phrases from a piece of text.
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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.