Incremental refresh in Power BI - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Incremental Refresh Basics

Which of the following best describes the main benefit of using incremental refresh in Power BI?

A It refreshes only new or changed data partitions, reducing refresh time and resource use.

B It refreshes the entire dataset every time to ensure data accuracy.

C It automatically deletes old data from the dataset to save storage space.

D It duplicates data to multiple tables for backup during refresh.

💡 Hint

Think about how refreshing only part of the data can save time.

Incremental refresh updates only the new or changed data partitions instead of the entire dataset, which saves time and computing resources.

❓ dax_lod_result

intermediate

DAX Filter for Incremental Refresh Policy

Given a table 'Sales' with a Date column, which DAX filter expression correctly defines a 5-year incremental refresh policy that keeps data for the last 5 years?

Power BI

FILTER('Sales', 'Sales'[Date] >= DATEADD(TODAY(), -5, YEAR))

A FILTER('Sales', 'Sales'[Date] >= TODAY() - 5 * 365)

B FILTER('Sales', 'Sales'[Date] > TODAY() - 5)

C FILTER('Sales', 'Sales'[Date] >= DATEADD(TODAY(), -5, YEAR))

D FILTER('Sales', 'Sales'[Date] <= DATEADD(TODAY(), -5, YEAR))

💡 Hint

Use DATEADD to subtract years from today for a date filter.

Option C correctly uses DATEADD to subtract 5 years from today and filters dates greater or equal to that, defining the last 5 years.

❓ data_modeling

advanced

Partitioning Strategy for Incremental Refresh

You have a large sales dataset with daily transactions over 10 years. To optimize incremental refresh, which partitioning strategy is best?

A Partition data by year to refresh only the current year partition.

B Partition data by month to refresh only the current month partition.

C Partition data by day to refresh only the current day partition.

D Do not partition; refresh the entire dataset each time.

💡 Hint

Consider a balance between refresh speed and partition management overhead.

Partitioning by month balances refresh granularity and management overhead, allowing efficient refresh of recent data without too many partitions.

🔧 Formula Fix

advanced

Troubleshooting Incremental Refresh Failure

You set up incremental refresh with a 1-year range but the refresh fails with an error about missing date column. Which option explains the most likely cause?

A The Power BI service is offline during refresh.

B The incremental refresh policy is set to refresh too many partitions at once.

C The dataset does not have any data for the last year to refresh.

D The date column used in the filter is not marked as Date/Time data type in the source.

💡 Hint

Check the data type of the date column used in the incremental refresh filter.

Incremental refresh requires the date column used in the filter to be of Date/Time type. If not, the refresh fails with a missing date column error.

🎯 Scenario

expert

Designing Incremental Refresh for Multiple Regions

Your dataset contains sales data for multiple regions with a 'Region' column and a 'Date' column. You want to apply incremental refresh only for the 'North' region, refreshing last 3 years of data. Which approach is correct?

A Create a filtered table for 'North' region and apply incremental refresh on its 'Date' column.

B Apply incremental refresh filter on 'Date' column only; filter region in report visuals.

C Apply incremental refresh filter on both 'Date' and 'Region' columns in the same policy.

D Use incremental refresh on the entire dataset without filtering by region.

💡 Hint

Think about how incremental refresh policies apply to tables and filters.

Incremental refresh policies apply at the table level. To refresh only 'North' region data, create a filtered table for that region and apply the incremental refresh policy on its date column.