PL-300 Prepare the data Practice Test 14 — 15 Questions

Question 1

You are designing a data model in Power BI. You have a 'Sales' table and a 'Date' table. The 'Sales' table has a 'SalesDate' column of type Date. You need to create a relationship between the tables, but the 'Date' table contains dates from 2010 to 2025, while the 'Sales' table only has data from 2020. Which type of relationship should you create to ensure optimal performance and correct filtering?

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

Accepted Answer

Many-to-one with single cross-filter direction from Date to Sales. A many-to-one relationship with single cross-filter direction from Date to Sales ensures that filters applied to the Date table propagate to the Sales table, which is the standard star schema design. This configuration optimizes query performance by avoiding unnecessary bi-directional filtering and correctly handles the date range mismatch, as the Date table's larger range does not affect filtering of Sales data.

Answer

Many-to-one with bi-directional cross-filtering

Answer

One-to-one (1:1)

Answer

Many-to-many (M:M)

Question 2

Which TWO of the following are valid reasons to use a calculated column instead of a measure in Power BI? (Select exactly two.)

Accepted Answer

You need to use the column in a relationship.. Calculated columns are evaluated row by row and stored in the model, making them available for use in relationships. Measures, in contrast, are evaluated at query time and cannot be used to define relationships between tables in Power BI.

Answer

You need to create a hierarchy for drill-down.

Answer

You need to perform a dynamic aggregation that changes with filters.

Answer

You need to calculate a running total.

Question 3

Which THREE of the following are best practices for data modeling in Power BI? (Select exactly three.)

Accepted Answer

Create a separate date table for time intelligence functions.. Option C is correct because a dedicated, continuous date table marked as a date table is required for reliable time intelligence functions such as TOTALYTD, SAMEPERIODLASTYEAR, and DATEADD, and it avoids gaps or duplicate dates that break those calculations. Option D is correct because primary key columns in dimension tables are used only for relationship joins, so hiding them from report view keeps the field list clean and prevents report authors from accidentally dragging surrogate keys into visuals. Option E is correct because a star schema with a central fact table surrounded by dimension tables delivers optimal VertiPaq compression, simpler DAX, and faster query performance than snowflaked or flat designs. Option A is not a best practice because bi-directional cross-filtering on all tables creates ambiguous filter paths, hurts performance, and can produce incorrect results; it should be used sparingly and only for specific many-to-many or security scenarios. Option B is not a best practice because calculated columns are computed at refresh, consume memory, and cannot respond to slicer context, whereas measures are evaluated at query time and are the preferred way to store dynamic business logic.

Answer

Use bi-directional cross-filtering relationships for all tables.

Answer

Store calculated logic in calculated columns rather than measures when possible.

Question 4

Refer to the exhibit. You are reviewing a Power Query script. The script fails with a 'DataSource.Error: Microsoft SQL: Login failed for user' error. Which step should you check first?

Accepted Answer

The credentials used in the Source step.. The error 'DataSource.Error: Microsoft SQL: Login failed for user' indicates an authentication failure when connecting to the SQL Server database. This occurs at the Source step, where Power Query first attempts to establish a connection using the provided credentials. Checking and correcting the credentials in the Source step is the logical first step because no subsequent data transformations (like removing columns, changing types, or filtering rows) can execute if the initial data source connection fails.

Answer

The column name in #"Removed Columns".

Answer

The data type transformation in #"Changed Type".

Answer

The filter condition in #"Filtered Rows".

Question 5

You are importing a large CSV file (200 MB) into Power BI Desktop. The import is very slow and sometimes fails. What should you do to improve performance?

Accepted Answer

Filter rows and columns during import using Power Query to reduce data size.. Filtering rows and columns via Power Query is the correct approach to reduce the data loaded into the Power BI Desktop model. However, query folding does not apply to CSV files — it is a technique for database sources. For CSV, Power Query reads the entire file but still reduces memory and processing overhead by discarding unnecessary columns and rows before loading into the model. This is a standard best practice for large flat files.

Answer

Use Power BI Service to import the file instead.

Answer

Remove all relationships before import.

Answer

Upgrade to Power BI Premium.

Question 6

You need to combine two tables: Sales and Products, where Sales has a ProductID column and Products has a ProductKey column. The tables have a many-to-one relationship. Which Power Query transformation should you use?

Accepted Answer

Merge Queries.. Merge Queries (C) is correct because it performs a join between two tables based on matching columns, which is exactly what is needed to combine Sales and Products using ProductID and ProductKey. This transformation supports many-to-one relationships and allows you to expand related columns from the Products table into the Sales table, enabling further data analysis.

Answer

Group By.

Answer

Append Queries.

Answer

Pivot Column.

Question 7

Your Power BI dataset uses DirectQuery to a SQL Server data warehouse. Users report that reports are slow. You need to improve performance without changing the data source. What should you do?

Accepted Answer

Create aggregated tables in Power BI using the Aggregations feature.. Creating aggregated tables in Power BI using the Aggregations feature allows you to pre-summarize data at a higher granularity while still using DirectQuery. This reduces the amount of data queried from the SQL Server data warehouse, improving report performance without changing the underlying data source or switching to Import mode.

Answer

Switch the dataset to Import mode.

Answer

Disable the 'Enable query reduction' option in Power BI Desktop.

Answer

Increase the memory limit of the on-premises data gateway.

Question 8

You have a table with a column 'Date' in text format (e.g., '2024-01-15'). You need to convert it to a date type. In Power Query, what is the best approach?

Accepted Answer

Change the column data type to Date in Power Query Editor.. Power Query's Change Type > Date (or the Date.From/Table.TransformColumnTypes operation) natively parses ISO-formatted text like '2024-01-15' into a true date type using the locale's date-parsing rules, which is the intended, one-step approach for this scenario. Splitting into year, month, and day (A) is unnecessary and error-prone when the text is already in a recognizable date format. Using the Excel Power Query add-in (B) is irrelevant since Power Query is already the tool in use, and creating a DAX calculated column with DATEVALUE (C) works in the data model rather than transforming the column at query time, which is less efficient and doesn't change the underlying column type.

Answer

Split the column into year, month, day and then combine.

Answer

Use the Excel Power Query add-in.

Answer

Create a calculated column in DAX using DATEVALUE.

Question 9

You have a dataset with a column 'FullName' containing values like 'John Doe'. You need to split this column into 'FirstName' and 'LastName' using the space delimiter. Which Power Query transformation should you use?

Accepted Answer

Split Column by Delimiter.. The 'Split Column by Delimiter' transformation in Power Query is specifically designed to divide a single text column into multiple columns based on a specified delimiter, such as a space. In this scenario, selecting the column 'FullName' and using 'Split Column > By Delimiter' with a space delimiter will correctly separate 'John Doe' into 'FirstName' (John) and 'LastName' (Doe). This is the standard approach for parsing delimited text within Power Query.

Answer

Merge Columns.

Answer

Extract Text.

Answer

Replace Values.

Question 10

You are connecting to an Azure SQL database using DirectQuery. The database has a large table with millions of rows. Users need to see aggregated data quickly. What should you implement to improve query performance?

Accepted Answer

Create aggregations in Power BI on the large table.. Creating aggregations in Power BI on the large table allows the DirectQuery model to pre-aggregate data at the source or in Power BI, reducing the volume of data queried and improving response times for aggregated results. This is a key performance optimization for DirectQuery models with large tables, as it avoids scanning millions of rows for every query.

Answer

Increase the memory limit of the Power BI Desktop.

Answer

Use a composite model with a smaller imported table.

Answer

Add indexes to the database table.

Question 11

You have a column 'ProductID' that contains integers. You need to ensure that this column is used as a key in relationships. What data type should the column have?

Accepted Answer

Whole Number.. In Power BI, relationship keys must be of a data type that supports exact matching and efficient indexing. Whole Number (integer) is the optimal type for primary keys in relationships, as it ensures unique, non-decimal values that Power BI can use for fast lookups and joins without precision issues.

Answer

Decimal Number.

Answer

Text.

Answer

Binary.

Question 12

Which TWO actions should you take to reduce the size of a Power BI dataset? (Choose two.)

Accepted Answer

Filter out rows that are not needed.. Option A is correct because filtering out rows that are not needed during import (for example, using Power Query filters or a WHERE clause in the source query) reduces the number of records loaded into the model, directly shrinking the dataset size in memory and on disk. Option B is correct because removing unnecessary columns during import eliminates entire columns of data from the model, which reduces both row-level storage and the columnar compression footprint in the VertiPaq engine. Option C is incorrect because disabling query folding typically hurts performance and does not reduce dataset size; folding pushes transformations back to the source. Option D is incorrect because adding calculated columns increases the model's size by storing additional materialized values. Option E is incorrect because DirectQuery does not store data in the model at all, but it is a connectivity mode change rather than a size-reduction action, and it does not reduce the size of an existing Import-mode dataset.

Answer

Disable query folding to improve performance.

Answer

Add calculated columns to precompute values.

Answer

Use DirectQuery instead of Import.

Question 13

Which THREE factors should you consider when choosing between Import and DirectQuery storage modes? (Choose three.)

Accepted Answer

Report interactivity and performance needs.. Option B is correct because Import mode loads data into the in-memory VertiPaq engine and typically delivers the fastest query performance and richest interactivity, while DirectQuery pushes queries to the source and can introduce latency, so the report's interactivity and performance needs directly drive the choice. Option D is correct because Import is bounded by dataset size limits (for example, 1 GB per dataset on Shared/Pro capacity, up to 400 GB on Premium/Fabric capacities) and memory, whereas DirectQuery has no such dataset size limit since data stays at the source, making data volume a key factor. Option E is correct because Import requires scheduled refreshes (up to 8 per day on Pro, 48 on Premium) and only shows data as of the last refresh, while DirectQuery reflects source changes in near real time, so refresh frequency and latency requirements are decisive. Option A is not a storage-mode factor because the Pro vs Premium license affects capacity features and refresh limits rather than the fundamental Import vs DirectQuery decision. Option C is irrelevant because color scheme and branding are purely visual design choices unrelated to how data is stored or queried.

Answer

User license type (Pro vs Premium).

Answer

Report color scheme and branding.

Question 14

Which TWO are valid ways to combine data from multiple sources in Power Query? (Choose two.)

Accepted Answer

Append Queries.. Append Queries (B) is correct because it stacks rows from two or more tables with matching or similar columns into a single table, which is the standard Power Query way to combine data vertically from multiple sources. Merge Queries (D) is correct because it joins two tables horizontally on one or more matching key columns, equivalent to a SQL JOIN, allowing data from multiple sources to be combined side by side. Pivot Column (A) is not a combining operation; it reshapes existing values in a single table from rows into columns. Create relationships in the data model (C) links tables for analysis in Power Pivot/DAX but does not itself combine or transform data within Power Query. Group By (E) aggregates rows within a single table (e.g., sum, count) and does not combine data from multiple sources.

Answer

Pivot Column.

Answer

Create relationships in the data model.

Answer

Group By.

Question 15

You need to create a date table in Power BI using DAX. Which function should you use to generate a continuous list of dates?

Accepted Answer

CALENDAR.. The CALENDAR function in DAX generates a single-column table of dates that starts from a specified start date and ends at a specified end date, creating a continuous list of all dates in that range. This is the correct function to use when you need to create a date table for time intelligence calculations in Power BI.

Answer

CALCULATE.

Answer

TODAY.

Answer

DATEVALUE.