PL-300 Practice Test 5 — 10 Questions

Question 1

Which TWO data sources can be used with DirectQuery mode in Power BI? (Choose two.)

Accepted Answer

SQL Server. DirectQuery mode in Power BI requires a data source that supports query pass-through to a backend engine, and SQL Server (option B) qualifies because Power BI issues native T-SQL queries against the relational database in real time without importing data. Azure SQL Database (option C) is likewise a supported DirectQuery source, since it is a cloud-based SQL Server engine that accepts the same T-SQL query folding and live connection pattern. In contrast, Excel workbook (A), CSV file (D), and SharePoint list (E) are file- or list-based sources that Power BI imports rather than querying live, so they are not valid DirectQuery data sources.

Answer

Excel workbook

Answer

CSV file

Answer

SharePoint list

Question 2

You are a Power BI developer at a healthcare organization. You are building a report that must comply with HIPAA regulations. You need to ensure that patient data is not exposed to unauthorized users. You plan to use Row-Level Security (RLS) with roles defined in Power BI Desktop. However, you also need to limit the data imported into the model to only necessary columns. The source is an Azure SQL Database with a table 'Patients' containing columns: PatientID, Name, SSN, Diagnosis, AdmissionDate, DischargeDate. Which two actions should you take? (Choose TWO)

Accepted Answer

Create RLS roles to restrict access by Diagnosis.. Option A is correct because defining RLS roles in Power BI Desktop and mapping them to users in the Power BI service enforces row-level filtering so that each user only sees the patient rows they are authorized to view, which is a core HIPAA minimum-necessary access control. Option E is correct because removing SSN and Name in Power Query before loading implements data minimization, ensuring sensitive identifiers are never imported into the semantic model where they could be exposed. Option B is not correct because storing credentials in the Power BI service is a connectivity/authentication practice, not a data-exposure control for this scenario. Option C is not correct because disabling query caching does not restrict which data is imported or who can see it. Option D is not correct because an encrypted connection protects data in transit but does not limit imported columns or enforce row-level access for report consumers.

Answer

Store data source credentials in the Power BI service.

Answer

Disable query caching for the dataset.

Answer

Use encrypted connection to the database.

Question 3

You are developing a Power BI semantic model for an e-commerce company. The source data comes from a CSV file containing order details: OrderID, OrderDate, CustomerID, ProductID, Quantity, UnitPrice, Discount, and ShippingCost. The file is updated daily. You need to model the data to support the following analyses: 1) Total sales amount (Quantity * UnitPrice - Discount) by product and month. 2) Average shipping cost per order by customer region (CustomerRegion is in a separate table). 3) Year-over-year comparison of sales. You need to create the measures and ensure optimal performance. What should you do?

Accepted Answer

Import both tables into Power BI, create a date table, and build measures using SUMX and time intelligence.. Importing both tables into Power BI and creating a star schema with a central fact table (Orders) and dimension tables (CustomerRegion, Date) enables efficient measure calculation. The measures can use SUMX to compute sales amount (SUMX(Orders, Orders[Quantity] * Orders[UnitPrice] - Orders[Discount])) and average shipping cost per order filtered by region via relationships. A separate date table is required for time intelligence functions like SAMEPERIODLASTYEAR for year-over-year comparison. Option A is suboptimal because adding calculated columns in Power Query increases storage and processing time; measures are preferable. Option B creates a flat denormalized table which duplicates customer region data, leading to larger model and slower performance. Option C is incorrect because DirectQuery is not supported on CSV files; Power BI requires data import or a connection to a database. Therefore, Option D is the best approach.

Answer

Use Power Query to add a calculated column for sales amount and shipping cost per order, then import.

Answer

Create a single table by appending the customer region to each row in the CSV using Power Query, then import.

Answer

Use DirectQuery on the CSV file to avoid storing data in Power BI.

Question 4

You are merging two queries in Power Query. Query 'Orders' contains columns: OrderID, CustomerID, OrderDate. Query 'Customers' contains columns: CustomerID, CustomerName, Segment. You need to add the CustomerName to the Orders query. The relationship between Orders and Customers is many-to-one. Which join kind should you use?

Accepted Answer

Left Outer. The goal is to retain all rows from the Orders table while adding CustomerName from the Customers table. A Left Outer join returns all rows from the first (left) table and only matching rows from the second (right) table, filling non-matches with null. Since the relationship is many-to-one, each OrderID may have a matching CustomerID, and you want to keep every order even if a customer is missing — exactly what Left Outer does.

Answer

Inner

Answer

Right Outer

Answer

Full Outer

Question 5

Which TWO of the following are valid data source types in Power BI that support DirectQuery? (Select TWO.)

Accepted Answer

Snowflake. Snowflake is a cloud-based data warehouse that supports DirectQuery in Power BI, allowing queries to be sent directly to Snowflake without importing data into Power BI's in-memory engine. This is possible because Snowflake provides a SQL-based interface that Power BI can connect to via its native connector, enabling real-time querying of large datasets.

Answer

Excel workbook

Answer

SharePoint Online List

Answer

JSON file

Question 6

You are a Power BI administrator. A user reports that their scheduled data refresh fails with error 'The data source credentials are no longer valid.' The dataset uses a SQL Server database with Windows authentication. What should you do first to resolve the issue?

Accepted Answer

Ask the user to update the data source credentials in the Power BI service dataset settings.. The first step is to ask the user to update the data source credentials in the Power BI service dataset settings. The error explicitly states that credentials are no longer valid, so refreshing them is the direct fix. This is a common issue when passwords expire or service accounts change.

Answer

Reinstall the on-premises data gateway on the server.

Answer

Reassign the dataset to a different Premium capacity.

Answer

Modify the dataset to use 'Impersonate the authenticated user' for data sources.

Question 7

You are the Power BI administrator for a large enterprise. The company has a Power BI Premium capacity with a single dataset that is used by multiple reports and dashboards. The dataset is refreshed daily at 3:00 AM, and the refresh typically completes within 2 hours. Recently, users have reported that the dataset is not showing the most recent data until after 6:00 AM. You investigate and find that the scheduled refresh is taking 4 hours to complete, and there are no errors in the refresh history. The dataset uses import mode and connects to an on-premises SQL Server data warehouse. The data model contains several large fact tables and multiple calculated tables and measures. What should you do to reduce the refresh time and ensure data is available by 5:00 AM?

Accepted Answer

Implement incremental refresh on the fact tables to refresh only new and changed data. Implementing incremental refresh on the fact tables allows Power BI to refresh only new or changed data instead of the entire dataset each time. This significantly reduces the refresh window, especially for large fact tables, because only the latest partition (e.g., today's data) is processed. Since the scheduled refresh starts at 3:00 AM and must complete by 5:00 AM, incremental refresh can cut the refresh time from 4 hours to under 2 hours by avoiding reprocessing historical data.

Answer

Remove all calculated tables and measures and replace them with calculated columns in Power Query

Answer

Change the dataset storage mode to DirectQuery to avoid the import process

Answer

Install an additional on-premises data gateway and configure load balancing

Question 8

Which TWO actions are required to set up a Power BI deployment pipeline with separate data sources for Development and Production?

Accepted Answer

Define dataset parameters in Power BI Desktop for data source values.. Defining dataset parameters in Power BI Desktop for data source values allows you to make the data source connection dynamic. These parameters can then be overridden at each stage of the deployment pipeline, enabling separate data sources for Development and Production without modifying the report file.

Answer

Assign the dataset owner to each stage workspace.

Answer

Re-enter data source credentials for each stage after deployment.

Answer

Create separate gateways for each stage.

Question 9

You have a Power BI data model with a table that contains duplicate rows. You want to remove duplicates in Power Query. Which transformation should you use?

Accepted Answer

Remove Duplicates. The correct option is B, Remove Duplicates, because it is the Power Query transformation specifically designed to eliminate duplicate rows from a table by comparing all columns (or selected columns) and keeping only the first occurrence of each unique row. In this scenario, the table contains duplicate rows, so applying Remove Duplicates directly addresses the requirement without altering the underlying data values. Replace Values (A) only substitutes specific text or numbers and does not remove rows. Filter Rows (C) excludes rows based on conditions but cannot identify duplicates across all columns. Group By (D) aggregates rows and would change the table structure rather than simply removing duplicate rows.

Answer

Replace Values

Answer

Filter Rows

Answer

Group By

Question 10

You need to create a calculated column that categorizes products based on price: 'Low' (<$10), 'Medium' ($10-$50), 'High' (>$50). Which DAX expression should you use?

Accepted Answer

SWITCH(TRUE(), Product[Price] < 10, "Low", Product[Price] <= 50, "Medium", "High"). Option C is correct because SWITCH(TRUE(), ...) evaluates each condition in order and returns the first TRUE result, so Product[Price] < 10 yields "Low", Product[Price] <= 50 yields "Medium", and the fallback "High" covers prices above 50. This pattern is the standard DAX way to implement multi-branch conditional logic with range comparisons. Option A is wrong because SWITCH with a scalar expression compares for equality, not ranges, so <10 and <=50 are not valid match values. Option B is wrong because LOOKUPVALUE retrieves a value from another table by matching columns and does not perform range-based categorization. Option D is wrong because DAX IF only takes three arguments (condition, true result, false result), so passing five arguments is invalid.

Answer

SWITCH(Product[Price], <10, "Low", <=50, "Medium", "High")

Answer

LOOKUPVALUE(Category, Product[Price], Product[Price])

Answer

IF(Product[Price] < 10, "Low", Product[Price] <= 50, "Medium", "High")