MB-910 Describe Dynamics 365 Customer Insights Practice Test 2 — 15 Questions

Question 1

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.

Answer

Create separate unifications for each region

Answer

Remove the email condition and use only customer name

Answer

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

Question 2

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.

Answer

The segment requires a relationship between tables that is not defined

Answer

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

Answer

The Purchase table does not have the correct data

Question 3

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.

Answer

Use an enrichment to standardize email addresses.

Answer

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

Answer

Run the data unification process, which automatically handles duplicates.

Question 4

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.

Answer

Define a relationship between the customer and purchase tables.

Answer

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

Answer

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

Question 5

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.

Answer

Export segments to the marketing platform.

Answer

Configure relationships between all data sources.

Answer

Enrich customer profiles with external data sources.

Question 6

You are a Dynamics 365 consultant for a multinational retail company. The company has recently deployed Dynamics 365 Customer Insights to unify customer data from point-of-sale (POS) systems, an e-commerce platform, and a loyalty program. The data ingestion pipeline is working, and unified customer profiles are being created. However, the marketing team reports that segments built using the 'LastPurchaseDate' attribute are not returning accurate results. Specifically, a segment defined as 'Customers who purchased in the last 30 days' includes customers whose last purchase was over 60 days ago, and excludes some customers who purchased yesterday. The data source for purchase transactions is updated daily via an incremental refresh. The 'LastPurchaseDate' field is computed from the transaction entity. Upon investigation, you notice that the unified profile entity shows a 'LastPurchaseDate' that is older than the most recent transaction for some customers. What is the most likely cause of this issue, and what should you do to fix it?

Accepted Answer

The unification process has not been re-run after the latest data refresh; schedule the unification to run after each data refresh.. Option C is correct because in Dynamics 365 Customer Insights, the unification process is a separate step that merges and deduplicates data from multiple sources into unified customer profiles. Even if the data source is refreshed incrementally, the unified profile entity will not reflect the latest transaction data until the unification process is re-run. Since the segment uses the 'LastPurchaseDate' from the unified profile, it will show stale data until unification is executed after each refresh.

Answer

The matching rules are incorrectly configured, causing the system to use an older transaction; update the matching rules.

Answer

The transaction data is in a different data source that is not linked to the profile; create a new data source and link it.

Answer

The incremental refresh is not configured correctly; reconfigure the data source to use full refresh instead.

Question 7

A retail company wants to unify customer data from their e-commerce platform, in-store POS system, and loyalty program into a single customer profile in Dynamics 365 Customer Insights. Which step should they perform first?

Accepted Answer

Ingest data sources into Customer Insights. Before any unification, segmentation, or analysis can occur, the raw customer data from the e-commerce platform, POS system, and loyalty program must first be brought into Dynamics 365 Customer Insights. Option C, 'Ingest data sources into Customer Insights,' is the foundational step because the system requires the data to be present before it can perform identity resolution, create unified profiles, or apply any downstream processes like enrichment or measure calculation.

Answer

Configure the enrichment service

Answer

Create a customer segment

Answer

Define measures for key performance indicators

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

Modify the matching rules to be stricter. Modifying the matching rules to be stricter reduces the number of false-positive duplicates by requiring more fields to match (e.g., adding email and phone to name matching). This preserves all data while preventing unnecessary merges, unlike deletion or manual processes. In Dynamics 365 Customer Insights, matching rules define which profile attributes are compared during unification, and stricter rules increase precision without data loss.

Answer

Delete all duplicate records from the source systems

Answer

Increase the confidence score threshold for matching

Answer

Run the deduplication process manually

Question 9

A marketing team uses Dynamics 365 Customer Insights to send personalized offers. They want to ensure that customers who have opted out of email marketing do not receive offers via email. What is the best practice?

Accepted Answer

Map consent attributes and respect them in marketing journeys. Customer Insights uses preference data to respect opt-outs. Option D is correct.

Answer

Use a measure to filter out opted-out customers

Answer

Manually remove opted-out customers from the segment each time

Answer

Create a segment excluding opted-out customers

Question 10

A company wants to use Dynamics 365 Customer Insights to predict which customers are likely to churn. Which feature should they use?

Accepted Answer

Predictive models. Option D is correct because Dynamics 365 Customer Insights includes a dedicated predictive model for churn. This feature uses historical customer data and machine learning to calculate a churn score for each customer, enabling proactive retention efforts. The other options focus on data preparation or basic segmentation, not predictive analytics.

Answer

Unification

Answer

Measures

Answer

Segments

Question 11

A company uses Dynamics 365 Customer Insights and wants to analyze customer lifetime value (CLV) across different product categories. They have transactional data with purchase amounts and dates. What should they create?

Accepted Answer

A measure to calculate CLV by product category. To analyze customer lifetime value (CLV) across different product categories, you need to create a measure in Dynamics 365 Customer Insights. Measures allow you to define custom calculations (e.g., sum of purchase amounts over time) and group them by attributes like product category, enabling the required analysis.

Answer

A segment for high-value customers

Answer

A new data source for CLV

Answer

An enrichment from a third-party data provider

Question 12

Which TWO actions are supported by Dynamics 365 Customer Insights?

Accepted Answer

Unifying customer data from multiple sources. Dynamics 365 Customer Insights is a customer data platform (CDP) designed to unify customer data from disparate sources such as CRM, ERP, and marketing systems into a single, comprehensive view. It supports enriching these unified customer profiles with external data (e.g., demographic or behavioral data from third-party providers) to enhance segmentation and personalization. Creating sales orders, managing inventory, and processing payroll are operational tasks handled by other Dynamics 365 applications like Sales, Supply Chain Management, and Finance, respectively.

Answer

Creating sales orders

Answer

Managing inventory levels

Answer

Processing payroll

Question 13

Which THREE outputs can be generated using Dynamics 365 Customer Insights?

Accepted Answer

Measures and KPIs. Option A is correct because Dynamics 365 Customer Insights allows users to define and compute Measures and KPIs from unified customer data. These are aggregated calculations (e.g., average order value, churn rate) that provide actionable insights into customer behavior and business performance, directly supporting data-driven decision-making.

Answer

Invoice documents

Answer

Purchase orders

Question 14

A retail company uses Dynamics 365 Customer Insights to unify customer data from their online store, loyalty program, and in-store POS systems. After unification, they notice that some customers have duplicate profiles. What is the most likely cause and recommended action?

Accepted Answer

Review and adjust the match rules used during the data unification process. Duplicate profiles after unification typically occur when the match rules (deduplication rules) are not configured precisely enough to identify that two records represent the same customer. In Dynamics 365 Customer Insights, the unification process uses these rules to merge profiles based on attributes like email or phone; if the rules are too loose or missing key identifiers, duplicates persist. Adjusting the match rules—such as setting exact match on email or using fuzzy matching with appropriate thresholds—resolves the issue without altering source data.

Answer

Re-run the data unification process without any changes

Answer

Manually delete duplicate customer records from the source systems

Answer

Contact Microsoft support to increase the deduplication limit

Question 15

A marketing manager wants to use Dynamics 365 Customer Insights to create a segment of 'High-Value Customers' based on a combination of purchase frequency and average order value. The data is unified, but the manager is unsure how to build the segment. What is the recommended approach?

Accepted Answer

Use the Segment Builder to create a dynamic segment using measures for purchase frequency and average order value. Option D is correct because Dynamics 365 Customer Insights provides a Segment Builder specifically designed to create dynamic segments based on measures like purchase frequency and average order value. This allows the marketing manager to define conditions using pre-built or custom measures without writing code, ensuring the segment updates automatically as data changes.

Answer

Write a SQL query in the data management interface

Answer

Use Power Automate to filter customers based on purchase history

Answer

Manually tag each high-value customer profile in the customer card