PL-300 Model the data Practice Test 2 — 15 Questions

Question 1

You are reviewing a Power Query M script used to create a table in Power BI. The script imports data from SQL Server, filters for orders in 2022, groups by ProductID to sum revenue, sorts descending, and takes the top 10. However, the table loads slowly. You need to improve performance. Which change should you make?

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

Accepted Answer

Modify the script to use a native SQL query that performs the filtering and aggregation on the server side.. Pushing filtering, grouping, and aggregation to SQL Server via a native query reduces the volume of data transferred to Power BI and leverages the database engine's optimized execution. This minimizes memory and processing overhead in Power Query, directly addressing the slow load time caused by performing these operations on imported data.

Answer

Add a Table.Buffer step before the filter to speed up subsequent operations.

Answer

Remove the sorting step because it is unnecessary for the final table.

Answer

Combine the filter, group, and sort into a single step using Table.Buffer.

Question 2

Drag and drop the steps to configure Row-Level Security (RLS) in Power BI Desktop into the correct order.

Accepted Answer

Create roles and define DAX filters in Power BI Desktop, then test the roles using the 'View as' feature, then publish the report to the Power BI service, then assign users or groups to the roles in the service.. RLS is configured in Power BI Desktop by creating roles with DAX filters, then tested locally before publishing and assigning users in the service.

Answer

Test the roles using the 'View as' feature, then create roles and define DAX filters in Power BI Desktop, then publish the report to the Power BI service, then assign users or groups to the roles in the service.

Answer

Create roles and define DAX filters in Power BI Desktop, then publish the report to the Power BI service, then test the roles using the 'View as' feature, then assign users or groups to the roles in the service.

Answer

Publish the report to the Power BI service, then create roles and define DAX filters in Power BI Desktop, then test the roles using the 'View as' feature, then assign users or groups to the roles in the service.

Question 3

Drag and drop the steps to create a date table in Power BI Desktop using DAX into the correct order.

Accepted Answer

1. Use CALENDAR to generate a date range. 2. Add calculated columns for Year, Month, Quarter, etc. 3. Mark the table as a date table.. Creating a date table involves using the CALENDAR function to generate a continuous range of dates, then marking it as a date table for time intelligence.

Answer

1. Mark the table as a date table. 2. Use CALENDAR to generate a date range. 3. Add calculated columns.

Answer

1. Add calculated columns. 2. Use CALENDAR to generate a date range. 3. Mark the table as a date table.

Answer

1. Use CALENDAR to generate a date range. 2. Mark the table as a date table. 3. Add calculated columns.

Question 4

Drag and drop the steps to create a quick measure in Power BI Desktop into the correct order.

Accepted Answer

Select the table for the measure, click Quick Measure in the Modeling tab, choose the calculation type, specify the fields, and then click OK.. Quick measures provide pre-built DAX calculations; you select the type and fields, and Power BI generates the measure.

Answer

Click Quick Measure in the Modeling tab, select the table for the measure, choose the calculation type, specify the fields, and then click OK.

Answer

Select the table for the measure, click Quick Measure in the Modeling tab, specify the fields, choose the calculation type, and then click OK.

Answer

Select the table for the measure, click Quick Measure in the Modeling tab, choose the calculation type, click OK, and then specify the fields.

Question 5

Match each Power BI component to its purpose.

Accepted Answer

Power BI Desktop: Used to create reports and data models.. These are core components of the Power BI ecosystem. Correct matches: Desktop creates reports, Service shares them, Report Server hosts on-premises. Common confusions involve Mobile and Gateway purposes.

Answer

Power BI Mobile: Used to create and edit reports.

Answer

Power BI Gateway: Used to connect cloud data sources to Power BI.

Question 6

Match each Power BI service feature to its description.

Accepted Answer

Dashboards: A single-page canvas for visualizing and interacting with data.. The correct matches are: Dashboards (single-page canvas), Workspaces (collaboration containers), Apps (bundled collections). Common confusions involve swapping these definitions.

Answer

Dashboards: A bundled collection of dashboards and reports for distribution.

Answer

Workspaces: A single-page canvas for visualizing and interacting with data.

Answer

Apps: Containers for collaborating on Power BI content.

Question 7

Match each Power BI security feature to its purpose.

Accepted Answer

Row-level security (RLS): Filters data at the row level based on user roles.. Row-level security (RLS) filters data at the row level based on user roles, while Object-level security (OLS) restricts access to specific tables or columns. The distractors swap these definitions.

Answer

Row-level security (RLS): Restricts access to specific tables or columns.

Answer

Object-level security (OLS): Filters data at the row level based on user roles.

Question 8

You are creating a star schema in Power BI. You have a fact table named Sales with columns: Date, ProductID, CustomerID, Quantity, and UnitPrice. You need to create a relationship between Sales and a Date dimension. Which column in the Date dimension should be used as the key to relate to the Date column in Sales?

Accepted Answer

DateKey. DateKey. In a star schema, the dimension table should have a unique key that matches the fact table's foreign key. The DateKey column is typically an integer (e.g., YYYYMMDD) or a date without time, ensuring a one-to-many relationship. Option A (DateID) is also plausible but less common; DateKey is the standard naming in Power BI. Option B (Date) could work if unique, but it may include time components causing granularity mismatch. Option C (FullDate) might include time, leading to mismatched granularity. Therefore, DateKey is the best practice for a clean star schema. The correct answer is D: DateKey.

Answer

DateID

Answer

Date

Answer

FullDate

Question 9

You have a Power BI data model with a table named Products that contains columns ProductID, ProductName, and CategoryID. You also have a table named Categories with CategoryID and CategoryName. You want to ensure that when a user filters by a category, all products in that category are shown, even if the product has no sales. What type of relationship should you create between Products and Categories?

Accepted Answer

Many-to-one from Products to Categories. Each product belongs to exactly one category, so the relationship is many-to-one from Products to Categories (equivalently one-to-many from Categories to Products). In Power BI, both are identical in implementation and cross-filter direction: filters propagate from the 'one' side (Categories) to the 'many' side (Products), which ensures all products in a selected category are shown even if they have no sales. Therefore, both B and D are acceptable.

Answer

Many-to-many

Answer

One-to-one

Question 10

You are building a Power BI report that uses a DirectQuery connection to a SQL Server database. You need to reduce the amount of data transferred to Power BI when users interact with visuals. What should you do?

Accepted Answer

Apply filters in Power Query to reduce rows. Applying filters in Power Query reduces the number of rows retrieved from the SQL Server database before the data is transferred to Power BI. With a DirectQuery connection, queries are sent to the source each time a visual is interacted with, so reducing the row count at the query level minimizes network traffic and improves report performance.

Answer

Increase the query timeout setting

Answer

Disable query reductions

Answer

Load all data into the Power BI model

Question 11

You are modeling data in Power BI that includes a table named SurveyResponses with columns: ResponseID, QuestionID, RespondentID, and AnswerText. Each respondent answers multiple questions. You need to create a measure that counts the number of unique respondents who answered a specific question. Which DAX measure should you use?

Accepted Answer

DISTINCTCOUNT(SurveyResponses[RespondentID]). DISTINCTCOUNT(SurveyResponses[RespondentID]) counts the number of unique RespondentID values in the table, which directly gives the count of unique respondents who answered a specific question when used in a filter context (e.g., with a slicer or visual grouping by QuestionID). This is the standard DAX pattern for counting distinct entities in a column.

Answer

COUNT(SurveyResponses[RespondentID])

Answer

COUNTROWS(SurveyResponses)

Answer

COUNTA(SurveyResponses[RespondentID])

Question 12

You are designing a Power BI data model for a sales analysis. The model includes a table named Sales with columns: OrderID, OrderDate, CustomerID, ProductID, Quantity, UnitPrice. You need to create a star schema. Which TWO tables should you create as dimension tables?

Accepted Answer

Calendar. In a star schema, the fact table (Sales) is surrounded by dimension tables that provide descriptive context. From the Sales columns, we identify the following dimensions: Calendar (from OrderDate), Product (from ProductID), and Customer (from CustomerID). Orders (OrderID) is typically a degenerate dimension or part of the fact grain, not a separate dimension. Therefore, there are three legitimate dimension tables in the options, so asking for exactly two is ambiguous and incorrect.

Answer

Sales

Answer

Orders

Question 13

You have a Power BI data model with a table named Employees that contains: EmployeeID, ManagerID, Department, and Salary. You need to create a parent-child hierarchy for the reporting structure. Which THREE actions should you perform?

Accepted Answer

Enable the 'Group on' feature in the visual to expand/collapse. Enabling the 'Group on' feature in a visual (such as a matrix or table) allows users to expand and collapse parent-child hierarchies, making the reporting structure interactive. This feature works with the hierarchy path created by calculated columns (Option B) and the self-referencing relationship (Option C) to display the drill-down behavior.

Answer

Create a separate table for managers

Answer

Set cross filter direction to Both on the relationship

Question 14

You have a Power BI data model with a table named Sales that includes columns: ProductID, SalesAmount, and SalesDate. You need to create a measure that calculates the total sales amount for the current quarter and compares it to the previous quarter. Which THREE DAX functions should you use?

Accepted Answer

PREVIOUSQUARTER. A is correct because PREVIOUSQUARTER is a time intelligence function that returns a table of dates for the previous quarter relative to the current filter context. When used inside CALCULATE, it shifts the evaluation of the SUM(Sales[SalesAmount]) to the prior quarter, enabling direct comparison with the current quarter's total.

Answer

DATEADD

Answer

DATESQTD

Question 15

You have a Power BI data model with a table 'Orders' that has columns: 'OrderID', 'OrderDate', 'CustomerName', 'Region', 'Product', 'Quantity', 'UnitPrice'. You want to create a measure that calculates total sales amount. Which DAX expression should you use?

Accepted Answer

Total Sales = SUMX(Orders, Orders[Quantity] * Orders[UnitPrice]). SUMX is an iterator function that evaluates the expression `Orders[Quantity] * Orders[UnitPrice]` for each row in the Orders table and then sums the results. This is necessary because DAX does not support direct multiplication of two columns inside SUM; SUM expects a single column reference, not an expression. SUMX performs row-by-row evaluation, which correctly computes the total sales amount as the sum of (Quantity × UnitPrice) across all orders.

Answer

Total Sales = COUNTROWS(Orders)

Answer

Total Sales = AVERAGE(Orders[Quantity]) * AVERAGE(Orders[UnitPrice])

Answer

Total Sales = SUM(Orders[Quantity] * Orders[UnitPrice])