PL-300 Exam Simulation 1 — 100 Questions

Question 1

You are connecting to a data source that contains Personally Identifiable Information (PII). You need to ensure that only authorized users can view the data in Power BI reports. Which TWO actions should you take?

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

Accepted Answer

Define row-level security (RLS) roles in Power BI Desktop.. Row-Level Security (RLS) allows you to restrict data access at the row level based on user roles defined in Power BI Desktop. By creating roles and using DAX filters, you can ensure that only authorized users see specific rows of PII data when the report is published to the Power BI service and users are mapped to roles.

Answer

Enable encryption at rest for the dataset.

Answer

Use Power Query to mask PII columns by replacing values with '***'.

Answer

Apply sensitivity labels to the dataset.

Question 2

Which TWO DAX functions can be used to filter data in a measure?

Accepted Answer

FILTER. (FILTER) is correct because it is a DAX function specifically designed to return a filtered subset of a table based on a logical condition, making it a primary tool for row-level filtering within measures. Option E (CALCULATE) is correct because it modifies the filter context of a measure by applying or removing filters, effectively filtering data through its filter arguments. Both functions directly control which rows are included in calculations.

Answer

VALUES

Answer

SUM

Answer

ALL

Question 3

You are reviewing a Power Query that imports data from SQL Server. The exhibit shows the M code. The SQL query filters records after a date, then Power Query filters rows with OrderQty > 10, and then groups by ProductID. What is a potential performance issue with this approach?

Accepted Answer

The filter on OrderQty > 10 should be included in the SQL query to reduce the amount of data transferred.. Pushing the `OrderQty > 10` filter into the SQL query reduces the amount of data transferred from SQL Server to Power Query. In Power Query, data is loaded into memory before transformations; filtering earlier in the source query minimizes memory usage and network latency, which is a key performance optimization in Power BI data loading.

Answer

The query will fail because the SQL query uses '>' with a string.

Answer

The SQL query should use a parameter for the date instead of a hardcoded value.

Answer

The grouping should be done in SQL to reduce data volume.

Question 4

Which TWO are best practices for optimizing Power Query performance? (Choose two.)

Accepted Answer

Disable the 'Enable background refresh' option in the query settings.. Disabling 'Enable background refresh' prevents Power Query from running queries in the background while you continue working in Power BI Desktop. This ensures that query execution is synchronous, which can improve performance by avoiding resource contention and allowing you to monitor progress directly. Background refresh can cause delays and unexpected behavior when multiple queries run simultaneously, especially with large data sources.

Answer

Merge tables as early as possible in the query to combine data.

Answer

Keep all columns in the table to avoid missing data.

Answer

Split columns by delimiter to normalize data.

Question 5

You have a Power BI report that uses a date table connected to a fact table. You need to ensure that all dates in the fact table are covered by the date table. Which relationship property should you configure?

Accepted Answer

Assume referential integrity. The 'Assume referential integrity' property, when enabled, tells Power BI that every value in the foreign key column of the fact table exists in the primary key column of the date table. This ensures that all dates in the fact table are covered by the date table, allowing Power BI to use more efficient storage and query execution (e.g., INNER JOIN semantics) rather than a full OUTER JOIN.

Answer

Make this relationship active

Answer

Cardinality

Answer

Cross filter direction

Question 6

You are troubleshooting a Power Query transformation that groups sales data by ProductID. The query runs slowly and you suspect the filter is being applied after loading all rows. What change would improve performance by pushing the filter to the source?

Accepted Answer

Replace the first three lines with a native SQL query that includes the WHERE clause. Pushing filter logic to the source database via a native SQL query with a WHERE clause reduces the amount of data loaded into Power Query. This leverages query folding, which allows the source (e.g., SQL Server) to perform the filtering before data is transferred, significantly improving performance for large datasets.

Answer

Disable the 'Enable load' option for the SalesTable

Answer

Use CALCULATE in DAX to filter

Answer

Add a 'Table.Buffer' step after the filter

Question 7

Which THREE of the following are best practices for optimizing data load performance in Power BI?

Accepted Answer

Remove unnecessary columns and rows during the import process.. Removing unnecessary columns and rows during the import process reduces the amount of data loaded into the Power BI data model, which directly decreases memory usage and refresh time. By filtering out irrelevant data early in Power Query, you minimize the data volume that must be processed and stored, leading to faster load performance.

Answer

Split a large fact table into multiple smaller fact tables.

Answer

Use DirectQuery mode instead of Import mode to reduce data load time.

Question 8

You are connecting to a SharePoint folder containing 100 Excel files. Each file has a similar structure but different column names. What is the best practice to combine these files into a single table while preserving the data?

Accepted Answer

Use Power Query's 'Combine Files' feature, selecting a sample file and promoting headers, then transforming column names to a standard set.. Power Query's 'Combine Files' feature is designed specifically for this scenario: it uses a sample file to infer the transformation logic (e.g., promoting headers), then applies that logic to all files in the folder. By transforming column names to a standard set within the sample file step, you ensure consistent column names across all files, preserving data integrity while combining them into a single table.

Answer

Load each file as a separate table and create relationships in the model.

Answer

Use Power Query's 'Merge Queries' to join all files into one table.

Answer

Use 'Append Queries' to stack all files, then rename columns manually.

Question 9

A company wants to create a Power BI report that shows sales performance by region. The data contains a table 'Sales' with columns: Date, Amount, RegionID, and ProductID. They also have a 'Regions' table with RegionID and RegionName. They want to display a matrix visual with RegionName on rows and Year on columns, with the sum of Amount as values. However, the report displays only 'RegionID' instead of 'RegionName'. What is the most likely cause?

Accepted Answer

There is no active relationship between the Sales and Regions tables.. If there is no active relationship between the Sales and Regions tables, Power BI cannot use the RegionName from the Regions table to filter or group the Sales data. Instead, it defaults to displaying the RegionID from the Sales table, which is the only related field available in the visual. An active relationship must exist between the two tables on the RegionID columns for RegionName to appear in the matrix.

Answer

The relationship is configured as many-to-many.

Answer

The relationship direction is set to Both.

Answer

The RegionID column in the Sales table is hidden.

Question 10

Which TWO of the following are valid DAX functions for time intelligence? (Select two.)

Accepted Answer

DATEADD. DATEADD is a valid DAX time intelligence function that shifts dates forward or backward by a specified number of intervals (days, months, quarters, years). TOTALYTD is also a valid time intelligence function that calculates the year-to-date value of an expression. Both are part of the dedicated time intelligence function set in DAX, which requires a properly marked date table with continuous dates.

Answer

RANKX

Answer

CONCATENATEX

Answer

MINX

Question 11

You are a data analyst for a global retail company. The company uses Power BI Premium capacity. You are building a dataset that combines sales data from three sources:
1. An Azure SQL Database that stores transactional sales data (10 million rows per day, retained for 5 years).
2. A SharePoint Online folder containing monthly Excel reports from regional offices (each report has a different structure).
3. A Dataverse table that contains customer feedback scores.

Requirements:
- The dataset must support near real-time reporting for the current month's sales (maximum 15-minute latency).
- Historical sales data (older than current month) can be refreshed daily.
- Customer feedback scores should be updated every hour.
- The Excel reports from SharePoint must be combined into a single table with consistent columns.
- The final dataset should be optimized for fast query performance.

You need to design the data preparation strategy. What should you do?

Accepted Answer

Use a composite model: DirectQuery for the current month's sales data from Azure SQL, and Import mode for historical sales (with incremental refresh) and for customer feedback (with hourly refresh). Combine SharePoint files using Power Query and load them into the model using Import mode. Set up a DirectQuery connection for near real-time.. It uses a composite model to meet all requirements: DirectQuery for near real-time current-month sales (≤15-minute latency), Import mode with incremental refresh for historical sales (daily refresh), Import mode for customer feedback (hourly refresh), and Power Query to combine SharePoint Excel files into a consistent table. This approach balances real-time needs with query performance and refresh flexibility, leveraging Power BI Premium's composite model capabilities.

Answer

Use DirectQuery for all data sources and create views in Azure SQL to transform the SharePoint and Dataverse data. Use Power Query to combine SharePoint files in a view.

Answer

Import all data into Power BI using Import mode. Schedule refreshes every 15 minutes for the current month and daily for historical data.

Answer

Use Azure Data Factory to copy all data to Azure SQL Database, then connect Power BI using DirectQuery.

Question 12

You are designing a Power BI data model that includes a table named Sales with 10 million rows. You need to create a relationship between Sales and a Product dimension table. The Product table has 10,000 rows. Which configuration will provide the best query performance?

Accepted Answer

Create a one-to-many relationship from Product to Sales. A one-to-many relationship from Product (the dimension table with unique values) to Sales (the fact table with many rows) is the standard star schema design. This configuration allows Power BI to use the smaller Product table to filter the larger Sales table efficiently, leveraging in-memory columnar storage and automatic aggregations for optimal query performance.

Answer

Create a many-to-one relationship from Sales to Product

Answer

Do not create a relationship; use LOOKUPVALUE in measures

Answer

Create a relationship with cross filter direction set to Both

Question 13

Refer to the exhibit. You are setting up row-level security (RLS) for a Power BI dataset. The JSON snippet shows the role definition. A user reports that they see no data when they view the report. What is the most likely cause?

Accepted Answer

The LOOKUPVALUE function requires a relationship between tables.. The LOOKUPVALUE function is used without a relationship between the Sales and EmployeeRegion tables. LOOKUPVALUE does not create a relationship; it performs a lookup based on the current row context, which may not work correctly. In RLS, you typically use a filter that references a related table via a relationship. Option A is wrong because the role name can be any string. Option B is wrong because the syntax is valid. Option C is wrong because the column definitions are fine.

Answer

The filter syntax is incorrect; it should use CALCULATE.

Answer

The role name 'RegionManager' is reserved.

Answer

The 'SalesPerson' column should be used in the filter.

Question 14

You are building a Power BI model that includes a table 'Orders' with columns: OrderID, CustomerID, OrderDate, and TotalAmount. You also have a table 'Customers' with columns: CustomerID, CustomerName, and Segment. You need to create a relationship between Orders and Customers on CustomerID. Which relationship configuration should you choose to ensure that filtering Customers by Segment correctly filters Orders?

Accepted Answer

One-to-many relationship from Customers to Orders. A one-to-many relationship from Customers (one side) to Orders (many side) with single-direction cross-filtering ensures that filtering Customers by Segment correctly filters Orders. Option B (many-to-one) would reverse the cardinality and, by default, would not allow filtering from Customers to Orders. Option C is unnecessary and can cause ambiguity. Option D is inappropriate as one customer can have many orders.

Answer

Many-to-one relationship from Orders to Customers

Answer

Many-to-many relationship with a bridge table

Answer

One-to-one relationship

Question 15

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. The correct components are A, C, and D. A measure using CALCULATE, COUNTROWS, and DATESINPERIOD (A) is needed to dynamically count customers who churned in the last 30 days. A relationship between the date table and Churn Date (C) is required for time intelligence functions like DATESINPERIOD to work correctly. A separate date table marked as a date table (D) is necessary because Power BI time intelligence functions require a proper date table with continuous dates and a date type. Option B (a calculated column for 30-day flag) is not needed; the measure handles the filtering. Option E (a disconnected table with date range) is unnecessary because the regular date table relationship suffices.

Answer

A calculated column for 30-day flag

Answer

A disconnected table with date range

Question 16

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 17

You have a Power BI dataset with a large fact table. You need to optimize report performance when users filter by date. What should you do?

Accepted Answer

Mark the date table as a date table in Power BI.. Marking a date table as a date table enables time intelligence functions and better performance. Option B is wrong because hiding columns does not improve performance. Option C is wrong because disabling cross-filtering might affect user experience but not performance directly. Option D is wrong because summarizing the fact table reduces granularity, which may not be desired.

Answer

Hide all columns except the date column.

Answer

Disable cross-filtering between tables.

Answer

Summarize the fact table by month.

Question 18

Which THREE settings should you verify in the Power BI tenant admin portal to ensure that external users (guests) can access a published app?

Accepted Answer

Invite external users to your organization via Microsoft Entra ID.. To allow external users (guests) to access a published app in Power BI, three tenant settings must be enabled: 'Invite external users' (A) allows inviting guest users, 'Allow Azure AD external identities to access the Power BI service' (C) enables guest sign-in, and 'Allow sharing with external users' (B) permits sharing content with guests. Option E ('Allow external users to edit and manage content') is not required for viewing an app; it grants additional editing permissions. Option D controls whether guests appear in people pickers, which is unrelated.

Answer

Show external users in lists of suggested people.

Answer

Allow external users to edit and manage content in the organization.

Question 19

A data model contains a Date table and a Sales table. You need to create a measure that calculates total sales for the previous year. Which DAX function should you use?

Accepted Answer

SAMEPERIODLASTYEAR. SAMEPERIODLASTYEAR is the correct DAX function for calculating total sales for the previous year because it returns a set of dates shifted back by exactly one year while preserving the current filter context (e.g., month, quarter). This function is specifically designed for year-over-year comparisons and works seamlessly with a Date table marked as a date table in the model. Option B: DATEADD can also shift dates by one year but requires specifying the interval and number of intervals, making it less direct for this specific requirement. Option C: PARALLELPERIOD returns the entire parallel period (e.g., full year) regardless of current granularity, which does not preserve the same relative period. Option D: PREVIOUSYEAR returns the entire previous year, not the same period shifted back, so it does not maintain month-over-month or quarter-over-quarter comparisons.

Answer

DATEADD

Answer

PARALLELPERIOD

Answer

PREVIOUSYEAR

Question 20

You are preparing data from an Excel workbook that contains multiple sheets. Each sheet has a similar structure but different data. You need to combine all sheets into a single table in Power Query. What is the most efficient approach?

Accepted Answer

Use the 'Combine Files' approach with the workbook as a folder, then select all sheets.. The 'Combine Files' approach in Power Query treats the workbook as a folder, allowing you to select all sheets and automatically combine them into a single table. This is the most efficient method when multiple sheets have a similar structure, as it uses a single transformation step and handles dynamic sheet names without manual query creation.

Answer

Load each sheet as a separate query and then use 'Append Queries' to combine them.

Answer

Use 'Merge Queries' to join the sheets based on a common column.

Answer

Create a new query that references each sheet query and then merges them.