This MB-910 practice question tests your understanding of describe dynamics 365 customer insights. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 manufacturing company uses Dynamics 365 Sales and Customer Insights. They want to identify customers who are likely to churn based on support ticket volume and purchase recency. Which Customer Insights feature should they use to build this analysis?
Predictive models
Predictive models, such as churn models, use historical data to predict future behavior.
Measures
Why wrong: Measures are aggregations, not predictive models.
Segments
Why wrong: Segments group customers but do not predict churn.
Enrichment
Why wrong: Enrichment adds external data, but does not predict churn.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Predictive models
A is correct because Predictive models in Dynamics 365 Customer Insights use built-in machine learning to analyze historical data—such as support ticket volume and purchase recency—and generate a churn probability score for each customer. This allows the company to proactively identify customers at risk of churning without requiring custom data science work.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Predictive models
Why this is correct
Predictive models, such as churn models, use historical data to predict future behavior.
Related concept
Read the scenario before looking for a memorised answer.
Measures
Why it's wrong here
Measures are aggregations, not predictive models.
Segments
Why it's wrong here
Segments group customers but do not predict churn.
Enrichment
Why it's wrong here
Enrichment adds external data, but does not predict churn.
Common exam traps
The trap here is that candidates confuse Segments (which can be dynamic and based on conditions) with Predictive models, not realizing that Segments only filter existing data while Predictive models generate new probabilistic insights.
Detailed technical explanation
The Predictive models feature in Customer Insights leverages Azure Machine Learning to train a binary classification model on your customer data, outputting a churn score between 0 and 1. The model automatically selects relevant features (e.g., ticket frequency, days since last purchase) and handles data preprocessing, making it accessible to business analysts. A real-world scenario: a company could use this to trigger a retention workflow when a customer’s churn probability exceeds 0.7, automatically sending a discount offer.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Describe Dynamics 365 Customer Insights — This question tests Describe Dynamics 365 Customer Insights — Read the scenario before looking for a memorised answer..
The correct answer is: Predictive models — A is correct because Predictive models in Dynamics 365 Customer Insights use built-in machine learning to analyze historical data—such as support ticket volume and purchase recency—and generate a churn probability score for each customer. This allows the company to proactively identify customers at risk of churning without requiring custom data science work.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 24, 2026
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