MB-230 Extend Customer Service Practice Question
You are tasked with providing agents with a real-time view of customer sentiment during a chat session. Which feature should you implement?
⚠ Common exam trap
Candidates often confuse 'Sentiment Analysis' with 'Customer Insights' or 'AI Builder' models. You must specifically enable the Omnichannel-native feature to provide the real-time agent view.
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
✓
Real-time Sentiment Analysis via Omnichannel settings.
The Sentiment Analysis feature in Omnichannel for Customer Service provides real-time monitoring of customer emotions. By enabling this in the workspace settings, agents receive visual feedback on the customer's sentiment. This context is invaluable for agents to adjust their tone and resolution strategy, which directly impacts customer satisfaction scores and prevents escalations by allowing agents to intervene proactively when a customer becomes frustrated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Customer Insights predictive scoring.
Why it's wrong here
Customer Insights focuses on historical data and aggregate trends rather than real-time sentiment during a live chat. While powerful for marketing and long-term planning, it does not provide the instantaneous, per-message emotional feedback required by a support agent currently talking to a customer.
- ✓
Real-time Sentiment Analysis via Omnichannel settings.
Why this is correct
Real-time Sentiment Analysis is the native capability designed specifically for this purpose. It analyzes chat text in real-time, displaying a sentiment score and icon in the agent's interface, allowing them to instantly adapt their approach to the specific emotional state of the customer during the session.
- ✗
Custom Power Automate sentiment detection flow.
Why it's wrong here
Building a custom sentiment detection flow using Power Automate is redundant and inefficient because it requires manually piping chat messages into an AI service and then updating the UI. This introduces significant latency and lacks the integrated, native user experience provided by the built-in sentiment analysis tool.
- ✗
Azure Cognitive Services text-to-speech module.
Why it's wrong here
Text-to-speech modules are designed for voice output, not sentiment analysis of text-based chats. Using this module would not provide the emotional analysis required to understand the customer's sentiment, and it does not integrate into the Omnichannel agent dashboard as a native sentiment indicator component.
About these practice questions
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Microsoft exam blueprint
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