PL-300 Practice Test 32 — 15 Questions

Question 1

You are building a Power BI data model that combines Sales data from SQL Server and Marketing data from a CSV file. The Sales table has a unique 'OrderID' column, and the Marketing table has a 'CampaignID' column. You need to create a relationship between Sales and Marketing to analyze campaign effectiveness. What should you do?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Create a bridge table containing unique combinations of OrderID and CampaignID.. A bridge table resolves the many-to-many relationship between Sales (OrderID) and Marketing (CampaignID). Since there is no direct key match, creating a bridge table with unique combinations of OrderID and CampaignID allows Power BI to model the relationship correctly and analyze campaign effectiveness without data duplication or ambiguity.

Answer

Use an inactive relationship between Sales and Marketing and activate it with USERELATIONSHIP in measures.

Answer

Create a separate table for each campaign and relate to Sales.

Answer

Merge the Marketing table into the Sales table using a left outer join.

Question 2

A company has a Power BI semantic model that uses Import mode. The model contains a table with 10 million rows. The data source is a SQL Server view that takes 5 minutes to execute. The scheduled refresh is set to every hour. What is the likely impact on refresh performance?

Accepted Answer

Refresh will take at least 5 minutes plus data loading time.. The refresh process must first execute the SQL Server view to retrieve data, which takes at least 5 minutes, and then load that data into the Import mode model. The total refresh time is the sum of the query execution time and the data loading time, so it will be at least 5 minutes plus additional time for loading 10 million rows.

Answer

Refresh will fail due to timeout on the gateway.

Answer

The model will automatically use incremental refresh to split the load.

Answer

Refresh will complete in parallel with the view execution.

Question 3

Which TWO of the following are valid methods to combine data from multiple sources in Power BI?

Accepted Answer

Append Queries. Append Queries is a valid method to combine data from multiple sources in Power BI because it stacks rows from two or more tables into a single table, which is essential when you have similar data structures across different sources (e.g., monthly sales files). This operation is performed in Power Query Editor and corresponds to a UNION operation in SQL, making it a standard data preparation technique.

Answer

Pivot Column

Answer

Unpivot Columns

Answer

Group By

Question 4

You have a Power BI workspace named Sales. You need to ensure that only users in the Finance security group can view reports in this workspace, while members of the Sales team can edit and share content. What should you do?

Accepted Answer

Add Finance as Viewer, Sales as Member.. Workspace roles in Power BI are designed to grant specific permissions: Viewer allows read-only access, ideal for Finance who only need to view reports; Member allows editing and sharing, which matches the Sales team's requirements. Option B is wrong because Contributor role cannot share content, which Sales needs. Option C is wrong because Admin grants full control, including managing permissions, which is unnecessary and excessive. Option D is wrong because row-level security (RLS) controls data access within reports, not workspace-level permissions.

Answer

Add Finance as Contributor, Sales as Member.

Answer

Add Finance as Viewer, Sales as Admin.

Answer

Use row-level security to restrict Finance data, add both as Member.

Question 5

You are importing data from an Excel workbook that contains multiple sheets. Each sheet has similar structure but different data for different regions. You need to combine all sheets into a single table for analysis. What should you do?

Accepted Answer

In Power Query Editor, use Append Queries to combine the sheets.. The Append Queries operation in Power Query Editor is designed to combine rows from multiple tables or queries with similar column structures into a single table. Since each sheet in the Excel workbook contains data for different regions with the same structure, appending them stacks the rows, creating a unified dataset for analysis.

Answer

In Power Query Editor, use Merge Queries to combine the sheets.

Answer

Copy and paste the data from each sheet into a master sheet in Excel.

Answer

In Power Query Editor, use Group By to consolidate the data.

Question 6

You are creating a Power BI report that uses a composite model with DirectQuery and imported tables. Which two considerations should you keep in mind?

Accepted Answer

Some DAX functions may have limitations when used across storage modes. Option A is correct because in a composite model, DAX functions that traverse relationships between DirectQuery and Import tables (or that rely on certain query-folding capabilities) can be limited; functions like CALCULATE, time intelligence, and some iterators may not fully translate to the source, so cross-storage-mode calculations can behave differently or be restricted. Option C is correct because when a report needs aggregations that combine data from both DirectQuery and Import sources, Power BI must query the external source and then combine results locally, which can degrade performance due to network latency, source query cost, and limited query folding. Option B is incorrect because relationships can be defined across tables of different storage modes in a composite model; that is a core capability of composite models. Option D is incorrect for the same reason—DirectQuery tables can be related to imported tables as long as the relationship is valid and the model is configured as a composite model. Option E is incorrect because measures can be created on DirectQuery tables as well as imported tables; there is no requirement that all measures reside in imported tables.

Answer

Relationships can only be defined between tables of the same storage mode

Answer

DirectQuery tables cannot be related to imported tables

Answer

All measures must be created in the imported tables

Question 7

You are importing data from an Excel workbook. The workbook has multiple sheets. You want to combine two sheets that have the same columns but different row data. Which TWO Power Query operations can you use?

Accepted Answer

Append Queries. Append Queries (C) is correct because appending stacks the rows of two tables that share the same column structure, which is exactly the goal of combining two sheets with identical columns but different row data. Append Queries as New (E) is also correct because it performs the same row-stacking operation but outputs the result to a new query instead of modifying an existing one, preserving the original queries. Merge Queries (A) is incorrect because it joins tables horizontally by matching key columns, adding columns rather than rows. Group By (B) is incorrect because it aggregates rows into summary values, not concatenates datasets. Pivot Column (D) is incorrect because it reshapes data by turning unique row values into new columns, which does not combine row data from two sheets.

Answer

Merge Queries

Answer

Group By

Answer

Pivot Column

Question 8

Which TWO of the following are true about calculated columns in Power BI?

Accepted Answer

Calculated columns are computed using DAX formulas.. Option C is correct because calculated columns are created by writing Data Analysis Expressions (DAX) formulas in the Power BI model, and their values are computed row by row using that DAX expression. Option D is correct because calculated columns are materialized and persisted in the model (in-memory in the VertiPaq engine), so they consume memory and increase the model size. Option A is incorrect because calculated columns can absolutely be used as the key on one side of a relationship, provided their values are unique and valid. Option B is incorrect because calculated columns are evaluated at data refresh/processing time, not at query time; it is measures that are evaluated at query time. Option E is incorrect because calculated columns are not stored on disk and loaded on demand — they are stored in memory as part of the model, unlike DirectQuery or some other storage behaviors.

Answer

Calculated columns cannot be used in relationships.

Answer

Calculated columns are evaluated at query time.

Answer

Calculated columns are stored on disk and loaded on demand.

Question 9

You are using Copilot for Power BI to assist with data preparation. Which THREE tasks can Copilot help you with?

Accepted Answer

Generate DAX expressions for calculated columns and measures.. Copilot for Power BI is designed to accelerate data preparation and modeling by generating DAX expressions for calculated columns and measures (A), which is a core supported capability for authoring calculations from natural-language prompts. It can also create Power Query M code snippets for common transformations (B), helping users build or extend queries in the Power Query Editor. Additionally, Copilot can suggest transformations to clean and shape data (D), such as removing duplicates, changing types, or splitting columns, which directly supports data preparation. Options C and E are not Copilot tasks: incremental refresh policies and data source credentials are configured manually in Power BI Desktop or the Power BI service settings, not generated by Copilot.

Answer

Set up incremental refresh policies.

Answer

Configure data source credentials for refresh.

Question 10

Which TWO Power BI visuals can be used to display hierarchical data? (Select two.)

Accepted Answer

Decomposition tree. The Decomposition tree (C) is correct because it is specifically designed to explore hierarchical data by letting users drill down through dimensions in any order, expanding nodes to reveal contributing sub-levels. The Matrix (D) is correct because it natively supports hierarchical row and column groupings, enabling expand/collapse drill-down through levels such as Year > Quarter > Month. The Scatter plot (A) is incorrect because it plots numeric values on X and Y axes to show correlations, with no hierarchical structure. The Card (B) is incorrect because it only displays a single aggregate value with no drill-down capability. The Pie chart (E) is incorrect because it shows proportional parts of a whole in a flat, single-level format without hierarchy.

Answer

Scatter plot

Answer

Card

Answer

Pie chart

Question 11

Which THREE of the following are best practices for designing Power BI reports for mobile devices?

Accepted Answer

Use a single column of visuals. Option A is correct because a single column of visuals matches the vertical scrolling orientation of phones in the Power BI mobile app, so each visual fills the width and remains legible without horizontal panning. Option D is correct because large, easy-to-tap slicers respect touch-target sizing guidance, reducing mis-taps on small screens where precise pointing is difficult. Option E is correct because grouping related measures into one visual (for example, a multi-row card or a single chart with several measures) conserves scarce canvas space and reduces the number of separate visuals a user must scroll through. Options B and C are not best practices: many small text tables are hard to read on a narrow screen and force zooming, while placing visuals side by side shrinks each one and typically requires horizontal scrolling, which the mobile layout is designed to avoid.

Answer

Include many small text tables

Answer

Place visuals side by side to maximize space

Question 12

You are transforming a table that contains a 'Date' column in text format (e.g., '2026-01-15'). You need to create separate columns for Year, Month, and Day. Which THREE Power Query transformations can you use? (Choose three.)

Accepted Answer

Split Column by Delimiter using '-' as the delimiter.. Option B is correct because the text dates use a consistent '-' delimiter, so Split Column by Delimiter with '-' produces three separate columns that can be renamed Year, Month, and Day. Option C is correct because a custom column can call Date.Year, Date.Month, and Date.Day (after converting the text to a date type with Date.FromText or a type change) to derive each component reliably. Option E is correct because the Extract feature can pull fixed character ranges from the text, e.g., the first 4 characters for Year and subsequent characters for Month and Day, which works for the ISO-style 'yyyy-MM-dd' format. Option A is not appropriate because Unpivot rotates columns into attribute-value rows rather than creating separate Year, Month, and Day columns. Option D is not appropriate because merging a column with itself is a join operation that does not split or extract date parts.

Answer

Unpivot the Date column.

Answer

Merge the Date column with itself.

Question 13

Which THREE actions in Power Query Editor can improve the performance of data refresh? (Select three.)

Accepted Answer

Remove columns that are not used in the report.. Option C is correct because removing unused columns reduces the volume of data that Power Query must read, transform, and load into the model, lowering memory and refresh cost. Option D is correct because disabling 'Enable load' on intermediate queries prevents those staging tables from being materialized into the data model, so refresh avoids loading data that no report consumes. Option E is correct because applying filters as early as possible (ideally at the source or in the first steps) reduces the number of rows flowing through subsequent transformation steps, which is the most effective way to speed up refresh. Option A is not correct because sorting rows does not improve refresh performance in Power Query; compression benefits in the VertiPaq engine come from column cardinality and encoding, not from row order. Option B is not correct because merging queries before filtering typically increases the work done on larger, unfiltered datasets; filtering first reduces the rows that must be joined.

Answer

Sort data in ascending order to improve compression.

Answer

Merge queries before filtering.

Question 14

Which TWO actions can help reduce the size of a Power BI dataset when preparing data?

Accepted Answer

Aggregate transaction data to daily level. Option B is correct because aggregating transaction-level rows into daily summaries collapses many individual records into far fewer rows, directly shrinking the dataset's row count and memory footprint in the Power BI model. Option D is correct because removing unused columns reduces the model's columnar cardinality and compression overhead, since Power BI's VertiPaq engine stores and compresses each column separately, so eliminating unnecessary columns lowers overall model size. Option A is incorrect because retaining all historical data increases row volume and model size rather than reducing it. Option C is incorrect because calculated columns are materialized and stored in the model, adding to its size instead of shrinking it. Option E is incorrect because DirectQuery does not reduce dataset size—it leaves data in the source and queries it on demand, and it is a connectivity mode rather than a data-reduction technique.

Answer

Include all historical data

Answer

Add calculated columns

Answer

Use DirectQuery mode

Question 15

Which THREE factors should you consider when designing a Power BI data model for a star schema? (Select three.)

Accepted Answer

Fact tables should contain numeric measures and foreign keys. Option B is correct because in a star schema the fact table stores the quantitative, numeric measures (such as Sales Amount or Quantity) along with foreign keys that reference the dimension tables, forming the core of the model. Option D is correct because dimension tables hold the descriptive, textual attributes (such as Product Name, Category, or Customer City) used for slicing and filtering the measures. Option E is correct because star schema relationships are one-to-many, with the dimension on the "one" side and the fact table on the "many" side, filtered in a single direction from dimension to fact. Option A is incorrect because many-to-many relationships between dimensions are not a star schema design factor; they add ambiguity and are typically avoided or resolved via bridge tables. Option C is incorrect because dimension tables in a star schema are intentionally denormalized (flattened) to reduce the number of joins and improve query performance, not normalized to reduce redundancy.

Answer

Use many-to-many relationships between dimensions

Answer

Dimension tables should be normalized to reduce redundancy