Recently Asked Power BI Developer Interview Question at Indegene
As a Power BI enthusiast or developer, interview questions often delve into the technical intricacies of DAX (Data Analysis Expressions). Here’s a deep dive into a commonly asked question, recently posed to a 2+ year candidate for the Power BI Developer role at Indegene.
1. What is the difference between ALL, ALLSELECTED, and ALLEXCEPT functions?
Understanding these functions is key to managing filters effectively in your calculations.
ALL
➡️ Removes all filters applied to a table or column, including slicers, visuals, and external filters.
Example:
If filters are applied to Region and Product, using ALL(Sales)
ignores both.
One-liner: Removes all filters from the data.
ALLSELECTED
➡️ Removes filters inside a visual but respects filters from slicers or external visuals.
Example:
If a slicer sets Region = East and a chart filters Product = Bikes, ALLSELECTED(Sales)
keeps the slicer filter but ignores the chart filter.
One-liner: Keeps slicer filters, ignores visual filters.
ALLEXCEPT
➡️ Removes all filters except those specified.
Example:
If filters are applied to Region and Product, using ALLEXCEPT(Sales, Sales[Region])
retains only the filter on Region.
One-liner: Keeps only specific filters, removes the rest.
2. Which function is used to make an inactive relationship active for a specific calculation?
The USERELATIONSHIP function allows you to activate an inactive relationship between tables for a single calculation.
Real-World Scenario:
Imagine your sales data has two dates: Order Date and Ship Date, both connected to a Calendar table. The default active relationship is with Order Date, but you want to use Ship Date for specific calculations.
Example:
This formula temporarily activates the relationship with Ship Date for the calculation, while leaving other relationships unchanged.
Pro Tip: Use USERELATIONSHIP to handle multiple date dimensions in your data model.
3. How to optimize DAX calculations?
Optimizing DAX ensures that your Power BI reports perform efficiently, especially when handling large datasets.
Best Practices for DAX Optimization:
- Keep calculations simple: Break down large formulas into smaller, reusable measures.
- Use variables: Define intermediate results using
VAR
to avoid recalculating values.
VAR
to avoid recalculating values.- Avoid complex functions: Prefer straightforward functions like
SUM
instead of SUMX
when possible. - Filter early: Apply filters at the data source or as early as possible in your model.
- Minimize CALCULATE: Use
CALCULATE
sparingly and with simple filters. - Optimize your data model: Remove unnecessary columns and focus on a star schema for simpler calculations.
- Optimize your data model: Remove unnecessary columns and focus on a star schema for simpler calculations.
- Use smarter grouping: Instead of
FILTER
, use SUMMARIZE
or GROUPBY
for better performance. - Clean your data: Less data in the model means faster computations.
SUM
instead of SUMX
when possible.CALCULATE
sparingly and with simple filters.FILTER
, use SUMMARIZE
or GROUPBY
for better performance.Final Thoughts
Mastering these concepts not only helps you ace interviews but also makes you a better Power BI professional. Interviewers often look for not just theoretical knowledge but also practical understanding through real-world applications. Practice these DAX functions with datasets to ensure you’re confident in their usage.
Have more Power BI questions? Drop them in the comments and let’s solve them together!
#PowerBI #DAX #InterviewQuestions #DataVisualization
Comments
Post a Comment