Power BI Fundamentals and Microsoft Ecosystem Questions
Fundamentals of Power BI usage, including Power BI Desktop and Power BI Service, data modeling with DAX, report and dashboard design, data connectivity within the Microsoft ecosystem (Excel, SQL Server, Azure Synapse/Azure SQL Database, Azure Data Lake), and governance, security, deployment patterns, and best practices for BI solutions in Microsoft-centric environments
MediumTechnical
0 practiced
Explain how you would connect Power BI to Azure Synapse Analytics using DirectQuery for interactive reporting on very large datasets. Discuss performance best practices such as pushing aggregations/materialized views to Synapse, limiting visual complexity, enabling query reduction, and deciding when to use Synapse dedicated SQL pool versus serverless options.
EasyTechnical
0 practiced
Using Power Query (M) in Power BI Desktop, explain how you would: remove duplicate rows, unpivot a set of columns into attribute-value pairs, and change data types for many columns efficiently. For each step, mention whether the transformation usually preserves query folding and why that matters.
MediumTechnical
0 practiced
Explain how to configure Incremental Refresh in Power BI for a large historical fact table (for example, 5 years of daily data). Include the steps to create RangeStart and RangeEnd parameters in Power Query, apply the filter to the date column, set the incremental refresh policy in the Power BI Desktop model, and considerations for change detection and detecting updated rows.
MediumTechnical
0 practiced
Describe how to implement dynamic Row-Level Security (RLS) in Power BI that filters sales data based on the logged-in user's email. Assume a lookup table named 'dim_user' with columns [email] and [user_id], and discuss the DAX expression for the role, testing RLS, and differences in behavior between Import and DirectQuery modes.
MediumTechnical
0 practiced
You're handed a large Power BI model that is slow and consumes a lot of memory. Describe practical steps you'd take to reduce model size and improve performance: discuss removing unused columns, reducing column cardinality, changing data types, replacing calculated columns with measures where possible, introducing aggregation tables, and when to consider DirectQuery or composite models. Also explain how you'd validate improvements.
Unlock Full Question Bank
Get access to hundreds of Power BI Fundamentals and Microsoft Ecosystem interview questions and detailed answers.
Sign in to ContinueJoin thousands of developers preparing for their dream job.