Describe Dynamics 365 Customer Insights practice questions

Define unification rules

Why wrong: Unification rules are applied after data ingestion.

Set up connections to Power BI

Why wrong: Connections to Power BI occur after data is processed.

Create a customer segment

Why wrong: Segmentation requires unified profiles.

The data source credentials are expired

Why wrong: Expired credentials would cause a connection error, not fewer records.

The segment includes too many conditions

Why wrong: Number of conditions does not reduce records unexpectedly.

The customer profiles are not unified

Why wrong: Unification is unrelated to refresh timing.

Segmentation

Why wrong: Segmentation groups customers but does not predict.

Data profiling

Why wrong: Data profiling only summarizes data quality.

Data sources

Why wrong: Data sources are inputs, not analytical outputs.

Change the data retention policy to zero days

Why wrong: Retention policies do not immediately delete data.

Reconfigure Data sources to exclude the customer

Why wrong: Excluding from future imports does not delete existing data.

Manually delete records from the source systems

Why wrong: Source systems are outside Customer Insights; deletion must be within the platform.

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

Why wrong: A single condition cannot reference two different product rows; need to use multiple conditions or a subquery.

First unify the two tables into one view, then segment

Why wrong: Unification is for identity resolution, not for combining purchase details.

Export the data to Power BI and create the segment there

Why wrong: Segments should be created in Customer Insights for downstream use.

Data export

Why wrong: Export sends data out of the system.

Data profiling

Why wrong: Profiling assesses data quality.

Data transformation

Why wrong: Transformation changes data format, not identity resolution.

The CSV format is incompatible

Why wrong: Format issues cause parsing errors, not access denied.

The CSV file is too large

Why wrong: File size would cause a different error (timeout or size limit).

The schema does not match the mapping

Why wrong: Schema mismatch errors occur during ingestion, not connection.

Measures

Why wrong: Measures are for KPIs, not identity resolution.

Data sources

Why wrong: Data sources are inputs, not the process of combining.

Segments

Why wrong: Segments group customers after unification.

Data sources

Why wrong: Data sources are inputs.

Data unification

Why wrong: Unification does not generate predictions.

Segmentation

Why wrong: Segmentation alone does not predict churn.

Increase the number of data sources

Why wrong: More sources may increase unmatched records.

Skip the unification step

Why wrong: Unification is necessary for a single view.

Delete the unmatched records

Why wrong: Deletion is not a solution; data is lost.

Export data to Excel and deduplicate there

Why wrong: Deduplication should be done within the platform.

Create a segment of duplicates

Why wrong: Segments do not merge profiles.

Delete the duplicate profiles manually

Why wrong: Manual deletion is not scalable; use unification.

Oracle Database

Why wrong: Oracle is not a standard data source.

Salesforce

Why wrong: Salesforce is not a built-in connector in Customer Insights.

Adobe Analytics

Why wrong: Adobe Analytics is not a native data source.

Inventory management

Why wrong: Inventory management is not a feature.

Invoicing

Why wrong: Invoicing is not part of Customer Insights.

Data profiling

Why wrong: Profiling is for data quality, not retention.

Data sources configuration

Why wrong: Data sources are foundational but not directly for retention.

Data exports to external systems

Why wrong: Exports are for integration, not directly for retention.

Real-time data streaming

Why wrong: Real-time streaming is not required.

Power BI integration

Why wrong: Power BI is optional.

Create separate unifications for each region

Why wrong: This would not solve cross-region duplicates.

Remove the email condition and use only customer name

Why wrong: Name alone is less reliable.

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

Why wrong: Lowering threshold may increase false positives.

The segment requires a relationship between tables that is not defined

Why wrong: Relationships can be defined in data model.

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

Why wrong: NOT EXISTS can be implemented via subquery or conditions.

The Purchase table does not have the correct data

Why wrong: Assuming data is correct, logic is wrong.

Use an enrichment to standardize email addresses.

Why wrong: Incorrect. Enrichments add data from external sources but do not merge duplicates.

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

Why wrong: Incorrect. Relationships link tables for analysis, not for deduplication.

Run the data unification process, which automatically handles duplicates.

Why wrong: Incorrect. Unification is a guided process that includes matching, but it requires configuring matching rules; it does not auto-handle all duplicates.

Define a relationship between the customer and purchase tables.

Why wrong: Incorrect. Relationships are used to link tables, not to build segments.

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

Why wrong: Incorrect. Measures compute metrics but do not define a segment.

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

Why wrong: Incorrect. Enrichments add data but do not create segments.

Export segments to the marketing platform.

Why wrong: Incorrect. Exporting is an output, not a prerequisite.

Configure relationships between all data sources.

Why wrong: Incorrect. Relationships are part of data integration but not specifically required for predictive models.

Enrich customer profiles with external data sources.

Why wrong: Incorrect. Enrichment is optional for predictive models.