PL-300 Practice Test 35 — 15 Questions

Question 1

You have a Power BI dataset that refreshes daily from an on-premises SQL Server database. The refresh fails with an error 'The data source credentials cannot be used for the connection'. What is the most likely cause?

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

Accepted Answer

The credentials stored in the gateway for the SQL Server have changed or expired.. The error 'The data source credentials cannot be used for the connection' specifically indicates that the credentials stored in the on-premises data gateway for the SQL Server data source have changed or expired. When a Power BI dataset refreshes from an on-premises SQL Server via a gateway, the gateway uses stored credentials to authenticate; if those credentials are no longer valid (e.g., password was rotated), the connection fails with this exact error.

Answer

The on-premises gateway is not running.

Answer

The dataset is using DirectQuery and the connection string is incorrect.

Answer

The SQL Server is not accessible from the cloud.

Question 2

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?

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 3

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 4

You are merging two tables in Power Query: 'Orders' and 'Customers'. You want to include only rows from Orders that have a matching CustomerID in Customers. Which join kind should you use?

Accepted Answer

Inner join. An inner join in Power Query returns only rows from both tables where there is a match on the join key. Since you want to include only rows from Orders that have a matching CustomerID in Customers, the inner join is the correct choice. It filters out any Orders rows without a corresponding CustomerID in the Customers table.

Answer

Full Outer join

Answer

Right Outer join

Answer

Left Outer join

Question 5

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 6

You are designing a Power BI solution for a retail company. The data includes point-of-sale transactions with columns: TransactionID, StoreID, ProductID, Quantity, SalesAmount, TransactionDate. The company wants to analyze sales by hour of day. What is the best way to prepare the time dimension?

Accepted Answer

Create a separate Time dimension table with a one-to-many relationship to the fact table. Creating a separate Time dimension table with a one-to-many relationship to the fact table follows the star schema best practice for Power BI. This approach allows efficient filtering and grouping by hour without bloating the fact table with calculated columns, and it supports time-based analysis across multiple fact tables if needed. The Time dimension can include columns like Hour, HourBucket, or PeriodOfDay, enabling intuitive slicers and drill-downs.

Answer

Add a calculated column extracting hour from TransactionDate in the fact table

Answer

Create a date dimension only and ignore time

Answer

Use DAX measures to extract hour on the fly

Question 7

You are designing a Power BI data model for a sales analytics solution. The source data includes a 'Sales' fact table with millions of rows and dimension tables for 'Customer', 'Product', 'Date', and 'Salesperson'. You need to minimize the model size in Power BI. Which action should you take?

Accepted Answer

Remove any columns from the fact table that are not used in the model.. Removing unused columns from the fact table reduces the amount of data imported into the Power BI model, which directly minimizes model size. Each column consumes memory for compression and storage, so eliminating unnecessary columns reduces the model footprint. DirectQuery (Option B) would also reduce the amount of data stored in the model, but it changes the storage mode to query the source directly, which is not the intended approach for minimizing the size of an imported data model.

Answer

Set the 'Storage mode' of fact table to 'DirectQuery'.

Answer

Enable 'Include relationship columns' in the relationship settings.

Answer

Create a calculated column for row number.

Question 8

You are importing data from a large CSV file (5 GB) into Power BI. The import takes too long and you need to reduce the data volume. What is the most effective approach in Power Query?

Accepted Answer

Filter rows and remove unnecessary columns in Power Query.. Filtering rows and removing unnecessary columns in Power Query directly reduces the amount of data loaded into Power BI's data model. This is the most effective way to reduce data volume from a large CSV file, as it minimizes the data that Power Query must process and store, directly addressing the import time issue.

Answer

Disable loading of unrelated queries.

Answer

Increase query parallelism in options.

Answer

Enable 'Query Reduction' settings.

Question 9

You are loading data from a SQL Server database into Power BI. You notice that the import takes a long time because the source table contains many rows. You only need a subset of rows based on a date filter. What should you do to improve performance?

Accepted Answer

Use a SQL query with a WHERE clause in the Power Query Editor.. Using a SQL query with a WHERE clause in Power Query Editor pushes the date filter down to the SQL Server database, reducing the amount of data transferred over the network and imported into Power BI. This query folding technique leverages the database engine's indexing and processing power, which is far more efficient than filtering after import. By retrieving only the necessary rows upfront, you minimize both network latency and memory consumption in Power BI.

Answer

Remove unnecessary columns in Power Query.

Answer

Load all data and then apply a filter in Power BI.

Answer

Enable incremental refresh on the dataset.

Question 10

You are building a data model for a retail company. The 'Sales' fact table has a column 'Discount' that is a percentage (0 to 1). You create a measure 'Total Discount Amount' = SUM(Sales[Discount]) * SUM(Sales[Quantity]) * SUM(Sales[UnitPrice]). However, the measure returns incorrect results when multiple discount percentages exist in the same filter context. What is the issue?

Accepted Answer

The measure is performing aggregations at the wrong granularity; it should use SUMX to iterate over each row.. The measure uses SUM on each column individually, which aggregates all values in the filter context before multiplying. When multiple discount percentages exist, this incorrectly multiplies the total of all discounts by the total of all quantities and total of all unit prices, rather than computing discount per row. The correct approach is to use SUMX to iterate over each row of the Sales table, calculating Discount * Quantity * UnitPrice per row and then summing those row-level results, ensuring accurate granularity.

Answer

The measure contains a circular dependency.

Answer

The measure is referencing columns from different tables without proper relationships.

Answer

The Discount column should be of type Decimal instead of Percentage.

Question 11

Which TWO of the following are true about using the 'Mark as Date Table' feature in Power BI?

Accepted Answer

The date table must have a column with unique date values. Option C is correct because 'Mark as Date Table' requires you to designate a column that contains unique, contiguous date values with no gaps or duplicates, which Power BI uses as the basis for time-based calculations. Option E is correct because marking a table as a date table lets Power BI apply built-in time intelligence functions such as TOTALYTD, SAMEPERIODLASTYEAR, and DATESYTD correctly against that table's date column. Option A is wrong because marking a date table is not required for every relationship involving a date column; it is only needed when you want proper time intelligence behavior. Option B is wrong because the marked date column must contain unique values, so duplicates are not allowed. Option D is wrong because 'Mark as Date Table' does not generate Year, Quarter, Month, or Day columns; those must already exist in the table or be created separately.

Answer

It is required for any relationship involving a date column

Answer

The date column can contain duplicate dates

Answer

It automatically creates all date hierarchy columns (Year, Quarter, Month, Day)

Question 12

You have a Power BI report that uses a custom visual from AppSource. The visual is not rendering correctly. Which three steps should you take to troubleshoot?

Accepted Answer

Check if the visual version is compatible with your Power BI Desktop version. Option B is correct because custom visuals from AppSource are compiled against specific Power BI API versions, so a visual built for a newer API than your Power BI Desktop build can fail to render; confirming version compatibility is a standard first troubleshooting step. Option C is correct because updating the visual to the latest version from AppSource resolves known rendering bugs and ensures you have the most recent API-compatible build. Option E is correct because custom visuals require their expected data roles/fields to be bound; if required fields are missing or mapped incorrectly, the visual will render blank or incorrectly. Option A does not belong because disabling hardware acceleration addresses general rendering glitches in Desktop, not a specific AppSource custom visual's compatibility or data-binding issues. Option D does not belong because clearing the browser cache applies to the Power BI Service in a browser, not to troubleshooting a custom visual in Power BI Desktop.

Answer

Disable hardware acceleration in Power BI Desktop

Answer

Clear the browser cache

Question 13

You are creating a Power BI report to analyze customer churn. You have a table with Customer ID, Churn Date, and other attributes. You want to create a measure that calculates the number of customers who churned in the last 30 days. Which THREE components do you need?

Accepted Answer

A measure using CALCULATE, COUNTROWS, and DATESINPERIOD. Option D is correct because time-intelligence functions like DATESINPERIOD require a dedicated Date table that is marked as a date table, ensuring a continuous, unbroken date range for accurate period calculations. Option C is correct because the Date table must be related to the Churn Date column in the fact table, which is what allows the filter context from the date table to propagate to the churn records. Option A is correct because the measure itself is built with CALCULATE to modify filter context, COUNTROWS to count the churned customers, and DATESINPERIOD to return the set of dates covering the last 30 days. Option B is not needed because a calculated column flag is static and evaluated at refresh, and it cannot respond dynamically to slicers or report filter context the way a measure does. Option E is not needed because a disconnected table with a date range would not filter the fact table through a relationship, so it cannot drive the time-based calculation correctly.

Answer

A calculated column for 30-day flag

Answer

A disconnected table with date range

Question 14

You need to grant a user the ability to manage permissions on a Power BI workspace but not to view or edit the content. What minimum role should you assign?

Accepted Answer

Admin. The Admin role is the only workspace role that can manage permissions and membership, even though it also allows viewing and editing content. The minimum role required to manage permissions is Admin. Option A is incorrect because Contributor can view and edit content but cannot manage permissions. Option B is incorrect because Viewer can only view content and cannot manage permissions. Option C is incorrect because Member can view and edit content but cannot manage permissions.

Answer

Contributor

Answer

Viewer

Answer

Member

Question 15

Which TWO actions are best practices for optimizing Power BI data models?

Accepted Answer

Replace text-based relationship columns with integer keys.. Replacing text-based relationship columns with integer keys (surrogate keys) reduces storage size and improves join performance. Power BI's VertiPaq engine compresses integer columns far more efficiently than text columns, leading to faster query execution and smaller memory footprint.

Answer

Use calculated columns instead of measures where possible.

Answer

Hide columns that are not used in reports.

Answer

Use many-to-many relationships instead of bridge tables.