MB-910 Describe Dynamics 365 Customer Insights — 40 Questions

Question 1

A company wants to use Dynamics 365 Customer Insights to create a unified customer profile. The data sources include a CRM system, a loyalty program database, and web analytics. What is the first step they should take?

Accepted Answer

Configure data sources and ingest data. In Dynamics 365 Customer Insights, the first step to creating a unified customer profile is to configure data sources and ingest data. Without data ingestion, there is no customer data to unify, segment, or analyze. This foundational step connects the CRM system, loyalty program database, and web analytics to the platform, enabling subsequent unification and segmentation.

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Define unification rules

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Set up connections to Power BI

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Create a customer segment

Question 2

A marketing team notices that a segment based on 'high-value customers' returns fewer records than expected. The segment criteria include 'Total Purchase Amount > $500' and 'Last Purchase Date within 90 days'. The data source is updated nightly. What is the most likely cause?

Accepted Answer

The segment was not refreshed after data update. The segment criteria are evaluated against the data at the time the segment is created or last refreshed. Since the data source updates nightly, the segment must be manually or automatically refreshed to incorporate the new data. If the segment was not refreshed after the nightly update, it would still reflect the older, smaller dataset, resulting in fewer records than expected.

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The data source credentials are expired

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The segment includes too many conditions

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The customer profiles are not unified

Question 3

A manager wants to understand the lifetime value of customers and predict future purchases. Which capability of Dynamics 365 Customer Insights should they use?

Accepted Answer

Predictive models. Predictive models in Dynamics 365 Customer Insights use machine learning to analyze historical customer data, calculate lifetime value (CLV), and forecast future purchase behavior. This directly matches the manager's need to understand customer value and predict future actions, making option C the correct choice.

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Segmentation

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Data profiling

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Data sources

Question 4

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. Option C is correct because 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 5

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. Option C is correct because 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 6

Which feature in Dynamics 365 Customer Insights allows you to combine data from multiple sources into a single customer view?

Accepted Answer

Data unification. Data unification in Dynamics 365 Customer Insights is the feature specifically designed to combine data from multiple sources—such as CRM, ERP, and transactional systems—into a single, unified customer profile. It uses matching and merging rules to identify and consolidate records that represent the same customer, resolving duplicates and creating a 360-degree view. This is the core mechanism for achieving a single customer view from disparate data sources.

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Data export

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Data profiling

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Data transformation

Question 7

A Customer Insights administrator receives an error when trying to ingest a CSV file from Azure Blob Storage. The error message says 'Access denied'. What is the most likely cause?

Accepted Answer

The connection string or SAS token is invalid or expired. The 'Access denied' error when ingesting a CSV file from Azure Blob Storage into Dynamics 365 Customer Insights indicates that the service cannot authenticate to the storage resource. The most common cause is an invalid or expired connection string or Shared Access Signature (SAS) token, as these credentials are required for secure access to Azure Blob Storage. Without valid credentials, Customer Insights cannot read the file, regardless of its format, size, or schema.

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The CSV format is incompatible

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The CSV file is too large

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The schema does not match the mapping

Question 8

A company has multiple business units that each use a separate CRM system. They want to use Customer Insights to create a single view of customers across these systems. Which feature should they use?

Accepted Answer

Data unification. Data unification is the correct feature because it is specifically designed to merge customer records from multiple data sources (such as separate CRM systems) into a single, unified customer profile. This process uses matching rules and deduplication to resolve identities across disparate systems, enabling a 360-degree view of each customer.

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Measures

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Data sources

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Segments

Question 9

A marketing team wants to send personalized emails based on a customer's predicted churn risk. Which Customer Insights capability should they use?

Accepted Answer

Predictive models. Predictive models in Customer Insights use machine learning to analyze historical customer data and generate scores like churn risk. This enables the marketing team to automatically identify high-risk customers and trigger personalized email campaigns based on those predictions.

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Data sources

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Data unification

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Segmentation

Question 10

During data unification, a large number of records are marked as 'unmatched'. The administrator wants to reduce this number. What is the best approach?

Accepted Answer

Adjust matching rules to be less strict. Option B is correct because adjusting matching rules to be less strict (e.g., lowering the similarity threshold or using fuzzy matching instead of exact match) allows more records to be considered as duplicates and merged, thereby reducing the number of unmatched records. In Dynamics 365 Customer Insights, matching rules define the conditions under which records from different data sources are considered the same customer profile, and making them less strict increases the recall of the unification process.

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Increase the number of data sources

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Skip the unification step

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Delete the unmatched records

Question 11

A retailer uses Customer Insights to track customer interactions across online and offline channels. They notice that some customers have duplicate profiles. What is the first step to resolve this?

Accepted Answer

Run data unification with appropriate matching rules. Option D is correct because Customer Insights provides a built-in data unification process that uses matching rules to identify and merge duplicate customer profiles. This is the recommended first step to resolve duplicates, as it leverages the platform's deduplication capabilities without manual intervention or data export.

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Export data to Excel and deduplicate there

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Create a segment of duplicates

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Delete the duplicate profiles manually

Question 12

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

Accepted Answer

Microsoft Excel. Microsoft Excel is a valid data source type in Dynamics 365 Customer Insights because it supports importing data from flat files, including .xlsx and .csv formats, via the ingestion pipeline. This allows users to bring in customer data from spreadsheets for unification and segmentation without requiring a database connection.

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Oracle Database

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Salesforce

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Adobe Analytics

Question 13

Which THREE of the following are capabilities provided by Dynamics 365 Customer Insights?

Accepted Answer

Data ingestion from multiple sources. Option A is correct because Dynamics 365 Customer Insights provides a unified data ingestion pipeline that can connect to multiple sources such as Azure Data Lake, Dataverse, Power Query connectors, and custom APIs. This allows organizations to bring in customer data from CRM, ERP, transactional systems, and external sources into a single customer data platform (CDP) for analysis and activation.

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Inventory management

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Invoicing

Question 14

A company wants to use Customer Insights to improve customer retention. Which TWO features should they use?

Accepted Answer

Segments (based on churn risk). Segments based on churn risk (Option B) allow you to group customers who are likely to leave, enabling targeted retention campaigns. Predictive models, specifically the churn model (Option D), use historical data and machine learning to calculate a churn probability score for each customer, which directly feeds into those segments. Together, they provide the actionable intelligence needed to proactively intervene and improve retention.

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Data profiling

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Data sources configuration

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Data exports to external systems

Question 15

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 that the data types in your dataset match the model's expected schema—for example, numeric fields for regression or categorical fields for classification. If the data types are mismatched (e.g., a string where a number is expected), the model will fail to train or produce inaccurate predictions. This prerequisite ensures the underlying machine learning algorithms can process the data correctly.

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Real-time data streaming

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Power BI integration

Question 16

Contoso Ltd. is a multinational retailer using Dynamics 365 Customer Insights to unify customer data from three regional CRM systems (North America, Europe, Asia) and a global e-commerce platform. The company has configured data sources for each system and run data unification, but the unified customer profiles show a high number of duplicates (approximately 15% of profiles are duplicates). The administrator has tried increasing the match confidence threshold but the problem persists. Additionally, the company wants to ensure that customer consent preferences (opt-in for marketing) are synchronized across all profiles. The administrator is considering adjusting the unification rules. The current matching rules use only email address. What is the best course of action to reduce duplicates while maintaining data accuracy?

Accepted Answer

Add additional matching conditions such as phone number and postal code. Option B is correct because adding additional matching conditions (such as phone number and postal code) increases the precision of duplicate detection. With only email as the match condition, records that share the same email but belong to different individuals (e.g., family accounts) or have data entry errors are incorrectly merged. By requiring multiple identifiers to match, the system reduces false positives while still catching true duplicates, improving the overall accuracy of the unified customer profiles.

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Create separate unifications for each region

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Remove the email condition and use only customer name

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Lower the match confidence threshold to 50% to match more records

Question 17

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. Option A is correct because the condition `CartAdd.Timestamp > 7 days ago` selects records with timestamps newer than 7 days ago, which is the correct logic for identifying recent cart additions. However, the segment returns no members because the `NOT EXISTS` subquery checks for purchases with `Purchase.Timestamp > CartAdd.Timestamp`, meaning any purchase after the cart addition will exclude the customer. If customers have made purchases after adding items to the cart (even within the last 7 days), the segment will exclude them, resulting in zero members. The real issue is that the condition should be `CartAdd.Timestamp < 7 days ago` to find older carts that have not been purchased, but the given answer option A incorrectly states the timestamp condition is reversed; the actual problem is the logic of the `NOT EXISTS` subquery, not the timestamp direction.

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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 18

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.. Option D is correct because 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 19

A marketing manager wants to use Dynamics 365 Customer Insights to create a segment of customers who have purchased a product in the last 30 days and have a high lifetime value score (above 80). The data resides in a unified customer profile entity that includes 'purchase date' and 'lifetime value score' fields. How should the manager build this segment?

Accepted Answer

Use the segment builder with conditions: purchase date is in last 30 days AND lifetime value score > 80.. Option A is correct because Dynamics 365 Customer Insights allows users to build segments directly from the unified customer profile using the segment builder. The manager can add conditions on the 'purchase date' field (e.g., 'in the last 30 days') and the 'lifetime value score' field (e.g., 'greater than 80') to create a dynamic segment that automatically updates as data changes. This approach leverages the built-in time-based and numeric filters without needing additional relationships, measures, or enrichments.

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Define a relationship between the customer and purchase tables.

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Create a measure that calculates the average purchase date and lifetime value score.

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Run an enrichment to add external data and then manually filter the list.

Question 20

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.. To use predictive models in Dynamics 365 Customer Insights, you must first create a unified customer profile by matching and merging data from all sources into a single customer entity. This unified profile is the foundation for all AI and machine learning features, including predictive models. Without it, the system cannot correlate data across sources to generate accurate predictions.

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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.