MB-910 Describe Dynamics 365 Customer Insights Practice Test 4 — 15 Questions

Question 1

Match each Dynamics 365 deployment option to its description.

Accepted Answer

Dynamics 365 Online: Hosted on Microsoft's cloud infrastructure, managed by Microsoft.. Dynamics 365 offers three main deployment options: Online (cloud, managed by Microsoft), On-premises (locally managed), and Hybrid (combination). Common mistakes include swapping the definitions of Online and On-premises, or misrepresenting Hybrid as purely cloud.

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Dynamics 365 Online: Installed on the customer's local servers, managed by the customer.

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Dynamics 365 On-premises: Hosted on Microsoft's cloud infrastructure, managed by Microsoft.

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Dynamics 365 Hybrid: Exclusive cloud deployment with no on-premises components.

Question 2

A retail company wants to create a single view of each customer by unifying data from their e-commerce platform, in-store POS system, and loyalty program. Which Dynamics 365 Customer Insights capability should they use?

Accepted Answer

Customer Data Platform (CDP) capabilities. Dynamics 365 Customer Insights is a Customer Data Platform (CDP) that ingests, unifies, and resolves customer data from multiple sources—such as e-commerce platforms, POS systems, and loyalty programs—into a single, unified customer profile. This capability is specifically designed to create a 360-degree view of each customer by matching and merging records across disparate data sources.

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Connections

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Predictions

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Segmentation

Question 3

A marketing manager wants to use Customer Insights to send personalized offers based on predicted churn risk. What should they configure first?

Accepted Answer

Create a prediction model. To send personalized offers based on predicted churn risk, the marketing manager must first create a prediction model in Dynamics 365 Customer Insights. This model uses AI to analyze customer data and generate a churn risk score, which can then be used to define segments and trigger personalized offers. Without the prediction model, there is no churn risk data to base the offers on.

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Create a segment

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Configure data enrichment

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Define a measure

Question 4

Your Customer Insights environment has multiple data sources with conflicting customer IDs. Some records from the loyalty system use email as ID, while the POS system uses a customer number. Which matching rule strategy should you use to unify these records?

Accepted Answer

Use conditional matching rules based on email and phone. Conditional matching rules allow you to define multiple conditions (e.g., match on email if available, otherwise match on phone) to resolve conflicts between different customer ID formats. This is the recommended approach in Dynamics 365 Customer Insights when data sources use different identifiers, as it maximizes the likelihood of correctly unifying records without requiring a single common key.

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Use exact match on customer numbers only

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Set a priority order and keep the first source's ID

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Use fuzzy matching on first and last name

Question 5

A company wants to use Customer Insights to send a birthday discount email to customers whose birthday is today. What should they create?

Accepted Answer

A dynamic segment with a date condition. A dynamic segment with a date condition is the correct approach because it automatically evaluates customer data each time the segment runs, selecting only those whose birthday matches today's date. This ensures the email is sent to the correct recipients on the specific day without manual intervention, leveraging Customer Insights' real-time data refresh capabilities.

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A measure

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A static segment

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An enrichment

Question 6

Your Customer Insights instance has ingested data from a CRM and an e-commerce platform. After unification, some customers appear as duplicates. What is the most likely cause?

Accepted Answer

Matching rules are too loose. When matching rules are too loose, the system merges records that are not truly the same customer, creating duplicates after unification. In Customer Insights, matching rules define the conditions (e.g., exact match on email, fuzzy match on name) that determine whether records from different sources represent the same entity. Loose rules allow records with minor differences to be considered matches, leading to false positives and duplicate customer profiles.

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Matching rules are too strict

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Data sources have different field names

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Confidence level is set too low

Question 7

A marketing team wants to use Customer Insights to predict which customers are likely to purchase a new product within the next 30 days. They have historical purchase data, web browsing behavior, and demographic data. What type of prediction should they create?

Accepted Answer

Custom model (binary classification). The marketing team needs to predict a binary outcome (will purchase vs. will not purchase) within a specific time window (30 days). Customer Insights' Custom model (binary classification) is designed for exactly this scenario, allowing you to train a model on historical purchase data, web browsing behavior, and demographic data to predict a yes/no outcome. Product recommendation and conversion likelihood models are not available as built-in prediction types in Customer Insights, and churn prediction focuses on existing customers leaving, not new product adoption.

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Product recommendation model

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Customer churn model

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Conversion likelihood model

Question 8

A user wants to export a segment of high-value customers from Customer Insights to Dynamics 365 Marketing to send a campaign. What is the recommended way to do this?

Accepted Answer

Use the Dynamics 365 Marketing connector. The Dynamics 365 Marketing connector is the recommended, native integration between Customer Insights and Dynamics 365 Marketing. It enables direct, real-time export of customer segments without manual steps, preserving data integrity and supporting automated campaign triggers. This built-in connector is the designed path for high-volume, low-latency segment sharing.

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Use the Customer Insights API

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Export the segment as a CSV file and import it

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Use Power Automate to copy data

Question 9

Your Customer Insights data shows that some customer profiles have missing phone numbers. You have a third-party data provider that can supply phone numbers based on email addresses. Which capability should you use to add this data?

Accepted Answer

Data enrichment. Data enrichment is the correct capability because it allows you to enhance existing customer profiles by appending missing attributes—such as phone numbers—from an external third-party data provider. In Dynamics 365 Customer Insights, enrichment uses a matching key (e.g., email address) to pull in additional data from a configured provider without overwriting or merging entire records.

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Data merge

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Data import

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Data matching

Question 10

You are troubleshooting a Customer Insights unification process that fails to match records that you know belong to the same customer. Both sources contain a common email field, but many emails are in different formats (e.g., 'john.doe@contoso.com' vs 'jdoe@contoso.com'). What should you do?

Accepted Answer

Use fuzzy matching on email with normalization. Fuzzy matching with normalization allows Customer Insights to handle variations in email formats (e.g., 'john.doe@contoso.com' vs 'jdoe@contoso.com') by using similarity algorithms (like Levenshtein distance) and normalizing data (e.g., removing dots or case differences) to improve match accuracy. This directly addresses the mismatch caused by different email formats while still leveraging the email field for deduplication.

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Remove the email condition from matching rules

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Use exact match on email

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Preprocess data to standardize email formats

Question 11

Which TWO actions can you perform using Customer Insights measures? (Choose two.)

Accepted Answer

Track monthly revenue trends. Measures in Customer Insights are used for calculations and aggregations, such as tracking revenue trends (A) and calculating average order value per customer (E). Creating segments (B), unifying customer records (C), and enriching profiles with demographic data (D) are separate features, not measures.

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Create a segment of high-value customers

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Unify customer records from multiple sources

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Enrich customer profiles with demographic data

Question 12

Which THREE are prerequisites for setting up Customer Insights predictions? (Choose three.)

Accepted Answer

Unified customer profiles. Unified customer profiles (Option A) are a prerequisite for Customer Insights predictions because predictions rely on a consolidated, single view of each customer derived from multiple data sources. Without unified profiles, the AI models cannot accurately associate behaviors, transactions, and attributes to individual customers, which is essential for generating reliable predictions like churn or purchase likelihood.

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External data enrichment

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Real-time data streaming

Question 13

Which TWO data sources can be ingested into Customer Insights? (Choose two.)

Accepted Answer

Azure Synapse Analytics. Azure Synapse Analytics is a correct data source for Customer Insights because it is a cloud-based analytics service that can be used as a data source via a direct connection, allowing you to ingest large-scale data into Customer Insights for unification and segmentation. Dynamics 365 Sales is correct because Customer Insights can directly connect to Dynamics 365 Sales to bring in customer engagement data (e.g., leads, opportunities, accounts) for a 360-degree customer view.

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Microsoft Forms

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Microsoft Entra ID

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Power BI reports

Question 14

A customer administrator configures the unification shown. After running unification, they notice that some records from the Web_Submissions source have empty email fields. What will happen to those records?

Accepted Answer

They will remain as unmatched profiles. In Dynamics 365 Customer Insights, when a unification rule is configured and a record from a source (e.g., Web_Submissions) has an empty email field, that record cannot be matched to any existing customer profile based on the email attribute. Instead of causing an error or being deleted, the record is retained as an unmatched profile. Unmatched profiles are stored separately and can be reviewed, merged, or acted upon later, ensuring no data loss occurs during the unification process.

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They will cause an error and stop unification

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They will match to CRM contacts with empty email

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They will be deleted from the data source

Question 15

A data scientist reviews the custom prediction configuration shown. The prediction output includes a score between 0 and 1. What does a score of 0.9 indicate?

Accepted Answer

High likelihood of purchase within 30 days. In Dynamics 365 Customer Insights, the custom prediction model for purchase likelihood outputs a score between 0 and 1, where a higher score indicates a greater probability of the predicted event occurring. A score of 0.9 means the model predicts a 90% probability that the customer will make a purchase within the next 30 days, representing a high likelihood of purchase.

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Low likelihood of purchase within 30 days

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Model confidence in the prediction

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Churn risk score