Power BI Essentials: Common Interview Questions and Concepts Quiz

Test your understanding of core Power BI concepts with this quiz featuring easy-level multiple-choice questions often asked in interviews. Explore key topics including data modeling, schedules, DAX optimization, field parameters, and more to boost your Power BI knowledge.

  1. DAX Calculation Optimization

    Which DAX function should you use to optimize a calculation involving repeated expressions, like Total = a*b/c*d?

    1. SUMX
    2. VAR
    3. ALL
    4. MAX

    Explanation: The VAR function allows you to store the result of a calculation and reuse it, improving both performance and readability. MAX is used to find the maximum value and does not optimize repeated calculations. SUMX is used for row-wise calculations but isn't suited for variable storage. ALL is mainly used to remove filters in a calculation, not for optimization.

  2. Schema Types

    What distinguishes a star schema from a snowflake schema in data modeling?

    1. Star schema uses denormalized tables, snowflake uses normalized tables
    2. Star schema is slower than snowflake schema
    3. Star schema has only fact tables, snowflake has only dimension tables
    4. Star schema contains only text data

    Explanation: A star schema has denormalized dimension tables connected directly to the fact table, making queries efficient. In contrast, a snowflake schema normalizes dimension tables to reduce redundancy. Star schema does not just contain fact tables, and both schemas can be optimized for speed in different scenarios. The schema type is unrelated to containing only text data.

  3. Data Flows Purpose

    In Power BI, what is the main benefit of creating a data flow rather than loading data directly into a report?

    1. To limit data availability to one report
    2. To increase file size and processing time
    3. To reduce data security
    4. To reuse cleaned and transformed tables across multiple reports

    Explanation: Data flows allow transformation and reuse of data tables across different reports, promoting efficiency. Increasing file size or processing time is not a benefit. Data flows actually improve data availability across reports, not limit it. Security can be maintained or improved, rather than reduced.

  4. Manual Data Refresh

    What is the typical use case for manual refresh in Power BI?

    1. When you need to automate large dataset updates
    2. When working with small datasets that don’t require frequent updates
    3. For incremental data processing only
    4. When you never want data to change

    Explanation: Manual refresh is practical for smaller datasets that don't change often, letting users update data as needed. Automation and incremental processing are handled better by scheduled or incremental refresh. Manual refresh does not mean data will never change; it is updated on demand.

  5. Scheduled Refresh Requirements

    Which type of Power BI account is required to configure a scheduled refresh?

    1. Read-only account
    2. Guest account
    3. Free account
    4. Pro or Premium account

    Explanation: Scheduled refresh requires either a Pro or a Premium account to access advanced features like automatic data updates. A free account lacks these capabilities, while read-only and guest accounts cannot configure refresh options.

  6. Incremental Refresh Purpose

    Why would you use incremental refresh in a Power BI dataset?

    1. To refresh only new or changed data instead of the entire table
    2. To refresh data manually
    3. To increase redundant storage
    4. To delete all data from the dataset

    Explanation: Incremental refresh updates only parts of the data that have changed, which saves time and resources. It does not delete data, nor is it a manual process. Incremental refresh optimizes storage rather than increasing redundancy.

  7. Reporting Development Challenges

    Which of the following is a common challenge encountered when developing Power BI reports?

    1. No need for data transformations
    2. Always having perfect data
    3. Incomplete or inconsistent source data
    4. Having too much training material

    Explanation: Dealing with incomplete or inconsistent data from sources is a common issue that requires extra data cleaning and validation. Training material excess is not an issue when building reports, and having perfect data is rare. Data often requires various transformations before reporting.

  8. Field Parameters Utility

    What is the key benefit of using field parameters in Power BI?

    1. Preventing interaction between visuals
    2. Increasing storage costs
    3. Locking all fields to a fixed value
    4. Enabling users to dynamically switch between different dimensions or measures in visuals

    Explanation: Field parameters let users choose which fields appear in visuals, making reports adaptable. They do not lock fields, increase storage costs, or prevent visual interactivity; rather, they increase flexibility and user engagement.

  9. Default Value Display

    How can you ensure a visual, such as a card showing total sales, displays the overall total regardless of page filters or slicers?

    1. Delete all slicers from the page
    2. Use the ALL function to remove filters in a measure
    3. Apply more filters using CALCULATE
    4. Hide the visual

    Explanation: By using ALL in a DAX measure, you can ignore page filters so that the visual always shows the grand total. Adding more filters would not help and hiding or deleting elements would remove interactivity or relevant features.

  10. Fact Table Centralization

    In a data model using a star schema, where is the fact table typically located?

    1. Directly attached to the data source without relationships
    2. At the center of the schema connected to dimension tables
    3. Outside the schema
    4. Randomly placed with no connections

    Explanation: The fact table sits centrally in a star schema, surrounded by dimension tables to link all data points. Random or unconnected placements do not reflect best practice. Placing it outside the schema or without relationships prevents effective analysis.

  11. Data Flow Creation Location

    Where do you create data flows in Power BI?

    1. On an external database
    2. In the data gateway
    3. In the Power BI service (cloud platform)
    4. In Power BI Desktop only

    Explanation: Data flows are created in the Power BI service to facilitate sharing and reuse. Power BI Desktop is for building reports but does not manage data flows. Data gateways connect to sources, and external databases are not used for Power BI data flow creation.

  12. Normalized vs. Denormalized Tables

    Which statement about normalized tables in a snowflake schema is correct?

    1. They contain only numerical data
    2. They increase reporting speed over star schemas
    3. They store all data in a single large table
    4. They minimize data duplication and split dimensions into multiple related tables

    Explanation: Normalization in snowflake schemas organizes data into related tables to reduce redundancy. All data stored in one table is not normalization. Snowflake schemas can be slower to query. They handle all data types, not just numbers.

  13. Power BI Refresh Types

    Which Power BI refresh method would you use for ultra-large datasets with frequent new transactions?

    1. Scheduled refresh
    2. Visual refresh
    3. Incremental refresh
    4. Manual refresh

    Explanation: Incremental refresh is designed for large, frequently changing datasets to update only recent transactions. Manual refresh is inefficient for big data, scheduled refresh reloads the whole dataset, and visual refresh is not a type of data refresh.

  14. Scenario-Based DAX Variable

    If you have multiple measures using the same calculation in DAX, what should you use to avoid repeating expressions?

    1. Define the calculation as a VAR variable
    2. Use SUMMARIZE
    3. Write the calculation multiple times
    4. Apply GROUPBY

    Explanation: Defining a variable with VAR allows you to store and reuse calculations in DAX, making your measures cleaner. SUMMARIZE and GROUPBY are used for aggregation, not variable declaration. Repeating calculations reduces efficiency and maintainability.

  15. Multiple Report Reusability

    How do data flows in Power BI improve the reusability of data transformations?

    1. They limit data usage to one user only
    2. They allow cleaned and transformed data to be used in several reports
    3. They require transformations to be repeated for each report
    4. They prevent sharing of data operations

    Explanation: Once created, data flows can be reused in different reports, improving efficiency. Restricting data to one user or requiring repeated transformations are not benefits. Data flows are designed to promote, not prevent, data-sharing.

  16. Visualization Customization

    What can field parameters help a Power BI report user accomplish?

    1. Change which dimension or measure is displayed on a visual with a single selection
    2. Lock values to prevent user changes
    3. Restrict all visuals to one column
    4. Automatically generate DAX queries

    Explanation: Field parameters let users toggle between fields or metrics, making reports interactive and flexible. They do not restrict all visuals, lock values, or automate DAX creation. Instead, they boost customization in Power BI reports.