MB-910 Describe Dynamics 365 Customer Insights — 15 Questions

Question 1

A company uses Customer Insights to manage customer data. They want to ensure compliance with GDPR by allowing customers to request deletion of their data. What is the recommended approach?

Accepted Answer

Use the 'Delete data' option in Customer Insights admin portal. Customer Insights provides a built-in 'Delete data' option in the admin portal that allows administrators to delete all data for a specific customer profile, including associated activities and relationships, to comply with GDPR deletion requests. This feature ensures that the deletion is performed within the Customer Insights data store, which is the primary system of record for unified customer profiles, without requiring changes to source systems or retention policies.

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Change the data retention policy to zero days

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Reconfigure Data sources to exclude the customer

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Manually delete records from the source systems

Question 2

A business analyst wants to create a segment of customers who have purchased both a laptop and a mouse in the last 30 days. The data is stored in two separate tables: 'PurchaseHeader' and 'PurchaseLines'. What is the best approach?

Accepted Answer

Use the segment builder to add two conditions: one for product = Laptop and one for product = Mouse, both with date filter. Dynamics 365 Customer Insights - Data segment builder allows you to add multiple conditions on the same entity (PurchaseLines) using AND logic, filtering for both 'Laptop' and 'Mouse' within the last 30 days. This approach directly queries the transactional data without requiring table unification or external tools, leveraging the platform's native segment creation capabilities.

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Create a segment and use the 'AND' condition for both products

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First unify the two tables into one view, then segment

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Export the data to Power BI and create the segment there

Question 3

Which TWO of the following are valid data source types in Dynamics 365 Customer Insights?

Accepted Answer

Microsoft Excel. Microsoft Excel (B) is a valid data source type in Dynamics 365 Customer Insights, as the platform natively supports ingesting data from Excel workbooks (.xlsx) uploaded to Microsoft Dataverse or OneDrive, allowing business users to bring in customer data without engineering effort. Azure SQL Database (C) is also a valid data source type, since Customer Insights provides a first-party connector that connects directly to Azure SQL Database using server and database credentials to pull tabular customer data. Oracle Database (A) is not a supported connector in Customer Insights, so it cannot be configured as a data source. Salesforce (D) is likewise not a native data source type in Customer Insights, despite being a common CRM, so it is not selectable as a source. Adobe Analytics (E) is not a supported data source type either, as Customer Insights does not offer a connector for Adobe Analytics data.

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Oracle Database

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Salesforce

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Adobe Analytics

Question 4

Which THREE of the following are prerequisites for using Customer Insights predictive models?

Accepted Answer

Data types that match the model requirements (e.g., numeric, categorical). Option B is correct because Customer Insights predictive models require each input field to be mapped to a supported data type — for example numeric for measures and categorical/boolean for flags — so the model can process the features correctly. Option D is correct because predictive models are trained on historical data that contains known outcomes (labels), such as past churn or purchase results, which the model learns from to score new records. Option E is correct because Customer Insights enforces a minimum data volume (commonly around 1,000 records, and often more for reliable results) so the model has enough examples to train without overfitting. Option A is not required: predictive models run on ingested, unified customer profiles in batch, not on real-time streaming data. Option C is not required: Power BI integration is for visualizing and consuming insights, not a prerequisite for training or running the predictive models.

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Real-time data streaming

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Power BI integration

Question 5

Adventure Works Cycles uses Dynamics 365 Customer Insights to analyze customer behavior. They have ingested data from their online store, including browsing history, cart additions, and purchases. They want to create a segment of customers who have added items to the cart but not purchased in the last 7 days (abandoned cart). The data includes a 'CartAdd' table with fields: CustomerID, ProductID, Timestamp, and a 'Purchase' table with fields: CustomerID, ProductID, Timestamp, Amount. The segment is created with condition: CartAdd.Timestamp > 7 days ago AND NOT EXISTS (Purchase where CustomerID matches and ProductID matches and Purchase.Timestamp > CartAdd.Timestamp). However, the segment returns no members. What is the most likely reason?

Accepted Answer

The timestamp condition is reversed; it should be CartAdd.Timestamp < 7 days ago to find older carts. The condition `CartAdd.Timestamp > 7 days ago` selects records where the cart addition occurred **less than** 7 days ago (i.e., within the last 7 days). To find customers who added items to the cart more than 7 days ago without purchase, the condition should be `CartAdd.Timestamp < 7 days ago` (older than 7 days). The segment returns no members because it is looking for recent cart additions instead of older ones. The `NOT EXISTS` subquery logic is actually correct for abandoned carts (checking that no purchase occurred after the cart addition), but the primary issue is the reversed timestamp comparison.

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The segment requires a relationship between tables that is not defined

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The 'NOT EXISTS' clause is not supported in Customer Insights segments

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The Purchase table does not have the correct data

Question 6

A retail company wants to use Dynamics 365 Customer Insights to create a unified customer profile from data stored in their CRM, e-commerce platform, and loyalty program. The data includes customer names, email addresses, purchase history, and loyalty points. The company discovers that some customers appear multiple times with slight variations in their email addresses (e.g., 'john.doe@contoso.com' vs 'johndoe@contoso.com'). What should the company configure in Customer Insights to resolve this issue?

Accepted Answer

Create a matching rule with fuzzy matching on the email field.. Fuzzy matching in Dynamics 365 Customer Insights allows the data unification process to identify and merge records that have slight variations in email addresses, such as missing dots or other minor differences. This is achieved by configuring a matching rule that uses fuzzy logic to compare email fields, enabling the system to recognize 'john.doe@contoso.com' and 'johndoe@contoso.com' as the same customer and create a unified profile.

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Use an enrichment to standardize email addresses.

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Define a relationship between the customer entity and the email field.

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Run the data unification process, which automatically handles duplicates.

Question 7

A company uses Dynamics 365 Customer Insights to manage customer data from multiple sources. They plan to use the system to generate predictive models, enrich customer profiles with external data, and export segments to a marketing platform. Which TWO actions are required before they can use predictive models?

Accepted Answer

Create a unified customer profile from all data sources.. Predictive models in Dynamics 365 Customer Insights require a unified customer profile as their foundation, so option B is correct: the unification process merges records from all ingested sources into a single consolidated customer entity that the AI model uses as its training population. Option C is also correct because the model needs sufficient historical data, such as transaction history, to learn meaningful patterns and produce accurate predictions; without adequate volume and history, training cannot succeed. Option A is not required before using predictive models, since exporting segments to a marketing platform is a downstream activation step that happens after models and segments exist. Option D is not a prerequisite because relationships between data sources are configured during data ingestion and unification, not as a separate step required specifically for predictive modeling. Option E is not required because profile enrichment with external data is an optional enhancement that improves insights but is not a precondition for training predictive models.

Answer

Export segments to the marketing platform.

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Configure relationships between all data sources.

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Enrich customer profiles with external data sources.

Question 8

A company uses Dynamics 365 Customer Insights and notices that some customer records are duplicated after unification. They want to reduce duplicates without losing data. What should they do?

Accepted Answer

Run the deduplication process manually. Modifying the matching rules to be stricter reduces false-positive duplicate profiles by requiring more fields to match. This prevents over-merging of distinct customers, preserving data while eliminating unwanted duplicates.

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Modify the matching rules to be stricter

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Delete all duplicate records from the source systems

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Increase the confidence score threshold for matching

Question 9

A company is implementing Dynamics 365 Customer Insights and wants to ensure that customer data privacy regulations, such as GDPR, are adhered to. Which feature should they configure to allow customers to request deletion of their data?

Accepted Answer

Enable the data privacy feature to handle deletion requests. Dynamics 365 Customer Insights includes a dedicated data privacy feature that provides a built-in mechanism to handle data subject requests (DSRs) under GDPR. This feature allows administrators to process deletion requests by searching for a customer's data across the system and permanently removing it, ensuring compliance with privacy regulations without custom development.

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Configure consent management for each customer

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Set up data retention policies

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Create a customer segment for deletion flags

Question 10

A company has configured a data unification rule as shown in the exhibit. After running the process, they find that customers with the same email but different phone numbers are not being merged. What is the most likely reason?

Accepted Answer

The Email field values contain slight differences like leading/trailing spaces. Leading or trailing spaces in the Email field cause exact match comparisons to fail, even though the values appear identical visually. Customer Insights data unification rules use exact matching by default unless fuzzy matching is explicitly configured, so any whitespace discrepancy prevents the rule from recognizing duplicate records. Trimming or normalizing the Email field before matching would resolve this issue.

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Deduplication is not enabled for the rule

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The fuzzy match on Phone is overriding the exact match on Email

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The output entity is not correctly configured

Question 11

A company is planning to use Dynamics 365 Customer Insights to enrich customer profiles with external demographic data. Which TWO of the following are valid methods to bring in external data? (Choose two.)

Accepted Answer

Use an OData connector to pull data from a partner data provider. Option A is correct because Customer Insights supports OData connectors, which can be configured to pull external demographic data from partner data providers directly into the unified customer profile ingestion pipeline. Option C is correct because Power Query is the built-in data transformation and ingestion engine in Customer Insights, and it can connect to third-party demographic APIs (for example via Web/API connectors) to bring in external enrichment data. Option B is incorrect because Azure Data Lake is not restricted to Dataflows-only connectivity; Customer Insights supports multiple ingestion paths including connectors and Power Query. Option D is incorrect because Excel Online direct connection is not a supported external data ingestion method for this enrichment scenario. Option E is incorrect because LinkedIn Sales Navigator is not a supported data source for synchronizing demographic enrichment into Customer Insights.

Answer

Connect to Azure Data Lake only via Dataflows

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Import from Excel Online using a direct connection

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Synchronize data from LinkedIn Sales Navigator

Question 12

You are a Dynamics 365 consultant for a multinational retail chain with operations in the US, Europe, and Asia. The company uses Dynamics 365 Customer Insights to create a single view of the customer across all regions. Each region has its own instance of Dynamics 365 Sales and separate data sources (POS, e-commerce, loyalty). The marketing team wants to create a global segment of customers who have purchased in at least two different regions in the past year. Currently, Customer Insights is set up with separate data unification processes for each region, and the unified profiles are not linked across regions. You need to design a solution to achieve the global segment. What should you do?

Accepted Answer

Set up a new Customer Insights environment that ingests data from each regional environment via Power Query and configure a cross-region matching rule using a common identifier such as email. It leverages a dedicated Customer Insights environment to ingest data from each regional environment via Power Query, enabling a cross-region matching rule using a common identifier like email. This approach creates a unified global customer view without duplicating or manually reconciling data, directly supporting the creation of a segment of customers who have purchased in at least two regions.

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Re-create all data sources in a single Customer Insights environment and run unification globally

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Export all unified profiles from each region to a common Excel file and manually identify duplicates, then import the combined list as a new segment

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Use Power BI to combine the regional unified profiles and create the segment directly in Power BI

Question 13

A marketing team at a manufacturing company uses Customer Insights to create a segment of customers who have purchased a specific product in the last 30 days. They want to send a promotional email to this segment. However, the segment results are empty even though they know customers have purchased the product within that timeframe. What is the most likely cause?

Accepted Answer

The segment has not been refreshed since the purchases were ingested. In Dynamics 365 Customer Insights, segments are static snapshots of customer data at the time of creation or last refresh. Even if purchase data has been ingested into the system, the segment will not automatically include those new records until it is explicitly refreshed. Since the segment results are empty despite known purchases, the most likely cause is that the segment has not been refreshed after the purchases were ingested.

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The data retention policy is set to 7 days

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The data source is offline

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The segment definition is incorrect

Question 14

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 15

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