Skip to content

Module quiz: Optimize a model for performance in Power BI :Data Modeling in Power BI (Microsoft Power BI Data Analyst Professional Certificate) Answers 2025

Question 1

How does performance optimization in Power BI influence decision-making?

❌ It reduces the number of decisions to be made.
❌ It ensures that reports are visually appealing while maintaining data integrity.
❌ It introduces new ways to present data.
✅ It facilitates more timely and informed decisions due to faster data analysis.

Explanation:
Faster report performance allows stakeholders to access insights quickly, enabling timely and better-informed decisions.


Question 2

You need a solution to pinpoint the required information rapidly in your datasets. Which optimization technique should you use?

❌ Sorting
❌ Data visualization
❌ Filtering
✅ Indexing

Explanation:
Indexing speeds up data retrieval by optimizing how data is searched internally.


Question 3

Which of the following is a recommended strategy for managing high cardinality?

❌ Frequently changing the data type of columns.
❌ Expanding data columns to include more details.
✅ Summarization of data during transformation.

Explanation:
Summarizing data reduces the number of unique values, helping improve model size and performance.


Question 4

Why can bi-directional filters become resource-intensive in Power BI?

❌ They make simultaneous connections to all related tables.
❌ They occasionally trigger unnecessary DAX calculations.
✅ They allow the filter context to flow in both directions, complicating the data model.

Explanation:
Bi-directional filtering increases calculation paths and complexity, which can slow down performance.


Question 5

In DirectQuery connectivity, which type of data source is typically used to create aggregations?

✅ Only databases like SQL, Azure SQL.
❌ All types of flat files (CSV).
❌ Any data source supported by DirectQuery.

Explanation:
Aggregations in DirectQuery are supported mainly on relational databases such as SQL Server and Azure SQL.


Question 6

After configuring the storage mode of an aggregated table, which remaining steps are required before using it?

❌ Perform transformations and build relationships.
❌ Manage relationships and create DAX measures.
✅ Build relationships and manage aggregations.

Explanation:
To use aggregations, you must define relationships and configure aggregation mappings.


Question 7

You need real-time access to on-premises SQL Server while optimizing queries. What actions must you perform?
(Select all that apply)

❌ Query the original data source for all analytical needs.
✅ Connect Power BI via DirectQuery mode.
✅ Create aggregations based on DirectQuery sourced table.
❌ Import the data to Power BI memory.

Explanation:
Real-time access requires DirectQuery, and aggregations help optimize query performance.


Question 8

Which best describes a limitation of DirectQuery connectivity?

❌ Inability to create relationships.
✅ Limited data transformation in Power Query editor.
❌ High memory consumption.

Explanation:
DirectQuery has restricted transformation capabilities compared to Import mode.


Question 9

In DirectQuery connectivity, where is the data stored?

❌ In Power BI cloud storage.
❌ In Power BI memory engine.
✅ In the data source storage.

Explanation:
With DirectQuery, data remains in the source system and is queried in real time.


Question 10

Which are characteristics of an optimized data model for DirectQuery performance?

✅ A model with only the columns and fields required for analysis.
❌ A model with a list of custom measures only.
❌ A model that contains fewer relationships.

Explanation:
Reducing unnecessary columns minimizes data transfer and improves query efficiency.


🧾 Summary Table

Question Correct Answer Correct Option Key Concept
Q1 Faster, timely decisions D Performance impact
Q2 Indexing D Fast retrieval
Q3 Data summarization C Cardinality control
Q4 Bi-directional complexity C Filter performance
Q5 SQL-based databases A DirectQuery aggregations
Q6 Build relationships & manage aggs C Aggregation setup
Q7 DirectQuery + aggregations B, C Real-time optimization
Q8 Limited transformations B DirectQuery limitation
Q9 Data source storage C DirectQuery behavior
Q10 Minimal required columns A Model optimization