Dataflow entities in Power BI - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is a dataflow entity in Power BI?

A dataflow entity is a table or dataset created and stored in Power BI dataflows. It holds cleaned and transformed data that can be reused across multiple reports and dashboards.

beginner

How do dataflow entities help in data reuse?

Dataflow entities store data centrally in Power BI service, allowing multiple reports to connect to the same cleaned data without repeating data preparation steps.

intermediate

What is the main difference between a dataflow entity and a dataset?

A dataflow entity is stored in Power BI service as part of a dataflow and focuses on data preparation, while a dataset is a model used directly by reports and dashboards.

intermediate

Can you refresh dataflow entities independently from datasets?

Yes, dataflow entities can be refreshed separately in Power BI service, updating the stored data before reports that use them refresh their datasets.

beginner

What tool is used to create and manage dataflow entities?

Power Query Online is used within Power BI service to create, transform, and manage dataflow entities.

What is stored inside a dataflow entity?

A Cleaned and transformed data tables

B Visual report layouts

C User access permissions

D Raw data files only

Dataflow entities store cleaned and transformed data tables ready for reuse.

Which tool do you use to create dataflow entities in Power BI?

A Power Query Online

B Power BI Desktop

C Excel

D SQL Server Management Studio

Power Query Online is the tool inside Power BI service to create and manage dataflow entities.

Can multiple reports use the same dataflow entity?

A Only if reports are published on the same day

B No, each report needs its own data

C Only if reports are in the same workspace

D Yes, dataflow entities enable data reuse

Dataflow entities are designed to be reused by multiple reports.

What happens when you refresh a dataflow entity?

A User permissions are reset

B The stored data updates with fresh data

C The report visuals refresh automatically

D The dataflow entity is deleted

Refreshing a dataflow entity updates the stored data with the latest source data.

Which of these is NOT a benefit of using dataflow entities?

A Centralized data preparation

B Improved data reuse

C Automatic report design

D Independent data refresh

Dataflow entities do not design reports automatically; they focus on data preparation.

Explain what a dataflow entity is and how it supports data reuse in Power BI.

Think about how one clean data table can serve many reports.

Describe the process and tools used to create and refresh dataflow entities.

Focus on the online tool inside Power BI service.

Practice

1. What is the main purpose of a Dataflow entity in Power BI?

easy

A. To prepare and store data centrally for reuse across reports

B. To create visual charts and graphs

C. To write DAX formulas for calculations

D. To manage user permissions in Power BI

Solution

1. Step 1: Understand what Dataflow entities do

   Dataflow entities are designed to prepare and store data centrally so it can be reused in multiple reports.

2. Step 2: Compare options with this purpose

   Only To prepare and store data centrally for reuse across reports matches this purpose; others describe different Power BI features.

3. Final Answer:

   To prepare and store data centrally for reuse across reports -> Option A

4. Quick Check:

   Dataflow entities = Central data prep and reuse [OK]

Hint: Dataflow entities store reusable data, not visuals or formulas [OK]

Common Mistakes:

• Confusing dataflows with report visuals
• Thinking dataflows manage permissions
• Mixing dataflows with DAX calculations

2. Which syntax correctly describes how to create a new entity in a Power BI Dataflow using Power Query online?

easy

A. Upload Excel files without any transformation

B. Write a DAX formula in the Dataflow editor

C. Use SQL commands directly in the Dataflow interface

D. Select 'New Entity' and use Power Query editor to define data transformations

Solution

1. Step 1: Identify how entities are created in Dataflows

   Entities are created by selecting 'New Entity' and using Power Query online to transform data.

2. Step 2: Check other options for correctness

   DAX formulas and SQL commands are not used directly in Dataflows; uploading files without transformation is incomplete.

3. Final Answer:

   Select 'New Entity' and use Power Query editor to define data transformations -> Option D

4. Quick Check:

   New Entity + Power Query = Correct syntax [OK]

Hint: Dataflow entities use Power Query, not DAX or SQL directly [OK]

Common Mistakes:

• Trying to write DAX in Dataflow creation
• Assuming SQL commands run in Dataflow editor
• Ignoring the need for data transformation steps

3. Given a Dataflow entity that filters sales data for the year 2023 using Power Query, what will be the output if the filter step is removed?

medium

A. All sales data including years other than 2023 will be included

B. Only sales data for 2023 will be shown

C. No data will be returned because filter is mandatory

D. The Dataflow will show an error and not load

Solution

1. Step 1: Understand the role of the filter step

   The filter step limits data to sales from 2023 only.

2. Step 2: Predict output without the filter

   Removing the filter means no restriction, so all sales data from all years will be included.

3. Final Answer:

   All sales data including years other than 2023 will be included -> Option A

4. Quick Check:

   Removing filter = All data included [OK]

Hint: No filter means no restriction on data rows [OK]

Common Mistakes:

• Assuming filter removal causes error
• Thinking filter is mandatory for dataflow
• Believing only 2023 data remains without filter

4. You created a Dataflow entity but it fails to refresh with an error about missing credentials. What is the best way to fix this?

medium

A. Delete and recreate the Dataflow entity

B. Change the entity name to a simpler one

C. Update the data source credentials in the Dataflow settings

D. Remove all transformations from the entity

Solution

1. Step 1: Identify cause of refresh failure

   Missing credentials error means Power BI cannot access the data source due to invalid or missing login info.

2. Step 2: Apply correct fix

   Updating the data source credentials in Dataflow settings restores access and allows refresh.

3. Final Answer:

   Update the data source credentials in the Dataflow settings -> Option C

4. Quick Check:

   Fix missing credentials by updating login info [OK]

Hint: Check and update credentials when refresh fails [OK]

Common Mistakes:

• Recreating entity unnecessarily
• Changing names does not fix credentials
• Removing transformations won't fix access issues

5. You want to create a Dataflow entity that combines customer data from two different sources and removes duplicates. Which approach is best?

hard

A. Write a DAX measure to merge and deduplicate data

B. Use Power Query to append both sources, then apply 'Remove Duplicates' step

C. Load both sources directly into Power BI without transformation

D. Create two separate entities and join them in Power BI Desktop

Solution

1. Step 1: Understand combining data in Dataflow

   Power Query in Dataflows can append multiple sources into one entity.

2. Step 2: Remove duplicates in Dataflow

   Applying 'Remove Duplicates' step in Power Query cleans the combined data before reuse.

3. Step 3: Evaluate other options

   Joining in Power BI Desktop or using DAX is less efficient; loading without transformation keeps duplicates.

4. Final Answer:

   Use Power Query to append both sources, then apply 'Remove Duplicates' step -> Option B

5. Quick Check:

   Append + Remove Duplicates in Dataflow = Best practice [OK]

Hint: Combine and clean data early in Dataflow with Power Query [OK]

Common Mistakes:

• Trying to merge data only in Power BI Desktop
• Ignoring duplicates before loading data
• Using DAX measures for data prep instead of Dataflow