MB-910 Describe Dynamics 365 Customer Insights Practice Question
You are a data analyst for a financial services company that uses Dynamics 365 Customer Insights. Your organization wants to use the Customer Insights data to improve customer retention. You have access to transaction data, customer service call logs, and web browsing behavior data. You need to identify customers who are likely to churn in the next 30 days. The solution must use built-in AI capabilities. What should you do?
⚠ Common exam trap
Watch out — candidates often confuse a simple rule-based segmentation (Option D) with AI-driven prediction, or assume that any external tool like Azure Machine Learning or Power BI is required for advanced analytics, when the exam specifically tests knowledge of Customer Insights' out-of-the-box AI models.
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
✓
Use the built-in Churn Prediction model in Dynamics 365 Customer Insights to analyze the data and get churn scores for each customer.
Dynamics 365 Customer Insights includes a built-in AI-powered Churn Prediction model that analyzes transaction data, customer service logs, and web browsing behavior to generate churn scores for each customer. This model uses pre-trained machine learning algorithms specifically designed for customer retention scenarios, requiring no custom development or external tools. It directly meets the requirement to use built-in AI capabilities without manual intervention or export to other services.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Export all data to Azure Machine Learning and build a custom churn prediction model, then import the results back into Customer Insights.
Why it's wrong here
Incorrect: This is unnecessarily complex; Customer Insights already has a churn model.
- ✗
Use Dynamics 365 Customer Insights to export the data to Power BI, visualize churn patterns, and manually identify likely churners.
Why it's wrong here
Incorrect: This requires manual analysis and does not leverage AI.
- ✓
Use the built-in Churn Prediction model in Dynamics 365 Customer Insights to analyze the data and get churn scores for each customer.
Why this is correct
Correct: The AI model is prebuilt and designed for this purpose.
- ✗
Create a manual segmentation rule in Customer Insights based on customers with no purchases in the last 30 days.
Why it's wrong here
Incorrect: This does not use predictive AI.
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