Quiz PL-300: Microsoft Power BI Data Analyst

The correct answer is Import mode with incremental refresh. While the data volume is large, the
requirement for sub-second response times and reduced database load during peak hours indicates that
caching data locally in Power BI is the best approach. Incremental refresh allows you to manage the large
dataset efficiently by loading only new or changed rows, reducing refresh time and database impact.
DirectQuery would continuously hit the database and defeat the goal of reducing load. DirectLake requires data
to be in OneLake and is not universally available for all enterprise data warehouse sources as of September
2026. Dual mode adds complexity without meeting the stated requirements as effectively as Import with
incremental refresh.

The correct answers are Remove all rows with null customer IDs, Create a custom column that
standardizes all customer IDs to a consistent format, and Change the data type to text and use
padding or concatenation to normalize the format. When a column is intended as a foreign key for
relationships, null values must be eliminated because Power BI relationships cannot function reliably with null
values in key fields. Standardizing the format through a custom column or type conversion ensures consistent
matching with the dimension table. All three correct answers address the core requirement of preparing the
column for use as a relationship key. Keeping nulls and creating an 'Unknown Customer' category would work
for analysis but not for establishing clean relationships. Splitting into two columns would create a more
complex schema and still not resolve the core issue.

The correct answer is Apply row-level security (RLS) roles using a DAX filter on the Geography table.
RLS is the standard, efficient method for restricting data access at the row level within a single semantic model
without duplicating data or creating multiple models. By creating RLS roles with DAX expressions that filter the
Geography table (e.g., [Region] = 'North America' for the Finance role), you can assign users to the
appropriate role and they will see only the permitted data. Creating separate models would be wasteful and
defeat the purpose of a shared semantic model. Filtering in Power Query would permanently remove data from
the model, eliminating flexibility for future users or roles. Column-level security only hides columns, not rows,
and does not enforce data access restrictions.

The correct answer is Edit the interaction between the clustered column chart and the transaction
details table to set it as 'None'. Power BI allows you to configure interactions between visuals by selecting a
visual and then choosing whether other visuals should filter, highlight, or remain unaffected. By setting the
interaction to 'None', you disable cross-filtering from the column chart to the table while keeping the
interaction between the column chart and line chart intact. This is a direct, efficient solution that maintains all
visuals on the same page and data source. Recreating the table with a separate data source would add
unnecessary complexity. Moving the table to a different page would degrade user experience. Adding a manual
override slicer does not address the root cause of the unwanted auto-filtering behavior.

The correct answer is Create a custom date table with fiscal year, fiscal month, and prior year
columns, then use CALCULATE with DATESBETWEEN to build measures. A custom date table gives you
complete control over fiscal calendars, time zones, and complex period definitions. This is the best practice
approach for international organizations with non-standard calendar requirements. You can then build robust
time intelligence measures using CALCULATE and DATESBETWEEN that reference this custom table. While
DATEADD can work for simple scenarios, it requires the underlying date column to be marked as a date table in
Power BI, and it is less flexible for custom fiscal periods. Calculated columns on the fact table would bloat the
model and create maintenance challenges. Power BI's automatic time intelligence features work only with the
standard Gregorian calendar and do not accommodate fiscal calendars or multi-time zone scenarios.

The correct answers are Advise the team members to manually refresh their reports, Check the
refresh schedule of the semantic model and trigger a manual refresh, and Verify that you published
the model with the same name and to the same workspace. After republishing a semantic model, the
reports using that model may show cached data until the semantic model itself is refreshed (independent of
report refresh). Advising users to refresh their reports and clear cache helps ensure they see updated visuals.
Triggering a manual refresh on the semantic model ensures the underlying data is current. Verifying the name
and workspace consistency preserves the binding between the report and model, ensuring the report pulls
from the correct, updated source. Creating a new model with a different name would break the connection for
existing reports and require manual reconfiguration, adding unnecessary complexity. Removing and
recertifying a report does not trigger a data refresh; Power BI caches query results separately from certification
status.
CASE STUDY 1
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to
complete each case. However, there may be additional case studies and sections on this exam. You must manage
your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case
study. Case studies might contain exhibits and other resources that provide more information about the scenario that
is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make
changes before you move to the next section of the exam. After you begin a new section, you cannot return to this
section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the
content of the case study before you answer the questions. Clicking these buttons displays information such as
business requirements, existing environment and problem statements. If the case study has an All Information tab,
note that the information displayed is identical to the information displayed on the subsequent tabs. When you are
ready to answer a question, click the Question button to return to the question.
Overview
Litware, Inc. is an online retailer that uses Microsoft Power BI dashboards and reports.
The company plans to leverage data from Microsoft SQL Server databases, Microsoft Excel files, text files, and several
other data sources.
Litware uses Azure Active Directory (Azure AD) to authenticate users.
- Existing Environment
Sales Data
Litware has online sales data that has the SQL schema shown in the following table.

Tap the image to zoom
In the Date table, the dateid column has a format of yyyymmdd and the month column has a format of yyyymm. The
week column in the Date table and the weekid column in the Weekly_Returns table have a format of yyyyww. The
regionid column can be managed by only one sales manager.
Data Concerns
You are concerned with the quality and completeness of the sales data. You plan to verify the sales data for negative
sales amounts.
Reporting Requirements
Litware identifies the following technical requirements:
* Executives require a visual that shows sales by region.
* Regional managers require a visual to analyze weekly sales and returns.
* Sales managers must be able to see the sales data of their respective region only.
* The sales managers require a visual to analyze sales performance versus sales targets.
* The sale department requires reports that contain the number of sales transactions.
* Users must be able to see the month in reports as shown in the following example: Feb 2020.
* The customer service department requires a visual that can be filtered by both sales month and ship month
independently.

The correct answer is C (Select Column profile, and then select the sales_amount column). The key constraint is
to address the data concerns before creating the data model. Column profile and column distribution expose
value frequency, distinct and unique counts, errors, empty values, and statistics for the selected field. Those
diagnostics answer a data-quality question without changing the source values. This directly matches the
current study-guide objective 'Evaluate data, including data statistics and column properties'. A transformation
that replaces, removes, or filters values would change the data before the analyst has quantified the problem;
profiling is the diagnostic step. This keeps the query output reproducible at refresh and leaves the semantic
model with the grain and schema required by the report.
Microsoft Learn reference: https://learn.microsoft.com/en-us/power-query/data-profiling-tools | PL-300
objective: 'Evaluate data, including data statistics and column properties' | Study guide:
https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/pl-300

What determines the answer is the need to create a calculated column to display the month based on the
reporting requirements. Measures are evaluated in the current filter context, whereas calculated columns are
stored at refresh. The selected DAX must therefore return the requested result at the correct grain without
materializing unnecessary model data. Choose A (FORMAT('Date'[date], 'MMM YYYY')). A measure would not
create the persistent row-level field or table needed for sorting, categorization, relationships, or reuse in the
model. Microsoft lists this skill in the current PL-300 outline as 'Create calculated tables or columns'. It also
preserves predictable filter context and aggregation behavior when the result is used by several visuals or
report pages.
Microsoft Learn reference: https://learn.microsoft.com/en-us/dax/ | PL-300 objective: 'Create calculated tables
or columns' | Study guide: https://learn.microsoft.com/en-us/credentials/certifications/resources/stud-
-guides/pl-300

Scenario: Executives require a visual that shows sales by region.
Need to change the sales_id column from Varchar to Whole Number (Integer).

The sale department requires reports that contain the number of sales transactions.
The COUNTROWS function counts the number of rows in the specified table, or in a table defined by
an expression.
Reference:
https://docs.microsoft.com/en-us/dax/countrows-function-dax