Which technique is an example of denormalizing data?

Adding redundant columns to a table to avoid joins

Creating indexes on foreign key columns

Splitting a table into multiple related tables

Using views to hide query complexity

A Fabric lakehouse table contains customer records loaded from multiple source systems. The same customer can appear multiple times with slightly different names (e.g., 'Contoso Ltd' and 'Contoso Limited'). You need to create a single customer dimension for reporting, keeping the most recently updated record for each customer. What should you do?

Perform a self-join on CustomerName and keep only the first row alphabetically.

Use a UNION to combine all records and then apply a DISTINCT on all columns.

Use a Dataflow to group by a normalized customer key (e.g., derived from email or tax ID), and keep the row with the latest LastUpdated timestamp.

Use the Remove Duplicates transformation on the CustomerName column.

A retail analytics team uses a Fabric lakehouse to support a sales dashboard. The current table stores every transaction row with product, store, and date columns. The dashboard queries frequently filter by product category and aggregate sales by month. The team wants to reduce query time and simplify the model. What should they implement?

Denormalize the product and store attributes into the transaction table and remove the dimension tables.

Create a stored procedure that refreshes a new aggregated table by product category and month, and use that table in the dashboard.

Create a view that joins the transaction table to product and store dimension tables, and add a calculated month column.

Add a calculated column for month to the existing transaction table and create an index on that column.

Microsoft Certified:Fabric Analytics Engineer Associate

Domain 2Objective 2

Transform Data DP-600 Practice Questions (Page 12)

Part of the Prepare data domain, which accounts for 45–50% of the DP-600 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~18–33 in this domain), expect 6–11 from this objective — we provide 59 practice questions to prepare you well beyond it. (estimate)

59questions here

12free pages

15concepts

45–50%of the exam

Questions 56–59

56foundation · easy
Which technique is an example of denormalizing data?
Select an answer first
57application · medium
A Fabric lakehouse table contains customer records loaded from multiple source systems. The same customer can appear multiple times with slightly different names (e.g., 'Contoso Ltd' and 'Contoso Limited'). You need to create a single customer dimension for reporting, keeping the most recently updated record for each customer. What should you do?
Select an answer first
58foundation · easy
In Power Query, which operation combines two tables by appending rows from one table to another?
Select an answer first
59application · medium
A retail analytics team uses a Fabric lakehouse to support a sales dashboard. The current table stores every transaction row with product, store, and date columns. The dashboard queries frequently filter by product category and aggregate sales by month. The team wants to reduce query time and simplify the model. What should they implement?
Select an answer first

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Transform Data DP-600 Practice Questions (Page 12)

Questions 56–59

Which technique is an example of denormalizing data?

In Power Query, which operation combines two tables by appending rows from one table to another?

Keep studying — Prepare data