PL-300 Practice Test 31 — 15 Questions

Question 1

You are analyzing a Power BI dataset definition. The dataset refreshes but recently started failing with the error 'The 'OrderDate' column of the table 'Orders' has a date value that is out of range.' You need to diagnose the issue. What is the most likely cause?

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

Accepted Answer

The source table contains a date value that is not recognized as a valid DateTime by Power BI.. The error message indicates that a date value in the 'OrderDate' column of the 'Orders' table is out of the range supported by Power BI's DateTime type (1/1/1900 00:00:00 to 12/31/9999 23:59:59.999). This typically occurs when the source contains a date like '0001-01-01' or a future date beyond the upper bound, which Power BI cannot convert to a valid DateTime. Option A is correct because the source table has a date value that is not recognized as a valid DateTime by Power BI, causing the refresh to fail.

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The columns in the dataset definition do not match the source table.

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The connection string is invalid.

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The M expression has a syntax error.

Question 2

You have a Power BI report that includes a line chart showing monthly sales. You want to add a forecast for the next six months. Which feature should you use?

Accepted Answer

Analytics pane. The Analytics pane in Power BI provides built-in forecasting capabilities for time-series data. By selecting a line chart and adding a forecast from the Analytics pane, you can configure the forecast length (e.g., six months), confidence intervals, and seasonality, enabling predictive analysis directly within the visual.

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Filter pane

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Format pane

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Visualization pane

Question 3

Refer to the exhibit. You are reviewing a DAX measure in Power BI. The measure is intended to calculate total sales for the year 2024. However, when used in a visual with a slicer on 'Sales[Date]', the measure does not respect the slicer selection. What is the most likely reason?

Accepted Answer

The FILTER function overrides the slicer filter context.. The FILTER function in the measure creates a new filter context that overrides the existing slicer filter context on 'Sales[Date]'. When CALCULATE evaluates the expression, it applies the FILTER as a table modifier, which replaces any external filters on the Date column, causing the slicer to be ignored. This is a common DAX behavior where explicit filter arguments in CALCULATE take precedence over existing filter contexts.

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The CALCULATE function removes all filters by default.

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The measure should use ALL(Sales[Date]) to respect slicers.

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The DATE function syntax is incorrect.

Question 4

Your organization uses Power BI to analyze sales data stored in Azure SQL Database. The data model includes a fact table with millions of rows. To improve performance, you need to reduce the amount of data loaded into the model. Which action should you take?

Accepted Answer

Apply row-level filters in Power Query to import only relevant rows. Applying row-level filters in Power Query reduces the volume of data imported into the Power BI model by only loading rows that meet specific criteria. This directly minimizes the data footprint in memory, improving query and refresh performance, especially for fact tables with millions of rows stored in Azure SQL Database.

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Use calculated tables in DAX to summarize data

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Disable the Auto Date/Time feature

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Configure incremental refresh with a date filter

Question 5

You are merging two queries in Power Query: 'Orders' and 'Customers'. The 'Orders' table has a 'CustomerID' column, and 'Customers' has 'CustomerID' and 'Name'. You need to bring the 'Name' into 'Orders' but only for matching CustomerIDs; unmatched rows should be removed. Which join kind should you use?

Accepted Answer

Inner. The Inner join kind in Power Query returns only rows where there is a match in both tables based on the key columns. Since the requirement is to bring the 'Name' into 'Orders' only for matching CustomerIDs and to remove unmatched rows, the Inner join is the correct choice. It ensures that only orders with a corresponding customer in the 'Customers' table are retained, and the 'Name' column is added to those matching rows.

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Right Anti

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Full Outer

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Left Outer

Question 6

A Power BI administrator needs to enforce that all datasets published to the service use certified data sources only. Which two settings should be configured? (Choose two.)

Accepted Answer

Enable 'Certification' for dataflows in the Power BI tenant settings.. To enforce that all datasets use certified data sources only, an administrator should enable certification for data sources (Option C) and enable certification for dataflows (Option B). Option C allows data source owners to certify data sources, and Option B allows dataflow owners to certify dataflows. Combined, these settings promote the use of certified components. Option A (monitoring with Sentinel) only detects non-certified sources, it does not enforce. Option D (RLS) and Option E (B2B permissions) are unrelated to data source certification.

Answer

Use Microsoft Sentinel to audit Power BI activity logs and flag non-certified data sources.

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Configure row-level security (RLS) on all datasets.

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Set up B2B guest user permissions to restrict external data sources.

Question 7

A Power BI dataset is configured to use Import storage mode. The dataset includes a fact table with 100 million rows and several dimension tables. The report is slow when users interact with visuals. You need to improve query performance without changing the storage mode. Which action should you take?

Accepted Answer

Create aggregations on the fact table.. Creating aggregations on the fact table allows Power BI to pre-summarize data at higher granularity levels, reducing the amount of data scanned during query execution. Since the dataset uses Import mode, aggregations leverage the in-memory columnar storage to serve queries from pre-computed tables, significantly improving visual response times without altering the storage mode.

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Increase the scheduled refresh frequency.

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Reduce the number of dimension tables.

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Enable 'Load to report' for all tables.

Question 8

You have a Power BI dataset that combines sales data from two Excel files: Sales2023.xlsx and Sales2024.xlsx. Both files have the same schema. You need to combine them into a single table without duplicating rows. What is the best approach in Power Query?

Accepted Answer

Use Append Queries.. Append Queries in Power Query is specifically designed to combine rows from two or more tables with the same schema into a single table, stacking them vertically without duplicating rows. This operation is performed in the Power Query Editor (M language) and is the standard approach for unioning data from multiple sources during the data preparation phase, before loading into the Power BI data model.

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Use Union in DAX.

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Use Group By to summarize data.

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Use Merge Queries as a new query.

Question 9

You are modeling data from an Azure SQL Database into Power BI. The source table 'Sales' contains 10 million rows. You need to ensure that the data model supports fast query performance for a report that shows sales by month and product category. The report uses a slicer for year. What is the best practice for improving performance?

Accepted Answer

Create an aggregate table in Power BI that pre-aggregates sales by month and product category.. Creating an aggregate table in Power BI that pre-aggregates sales by month and product category drastically reduces the number of rows the report must scan, from 10 million to a much smaller set of aggregated rows. This enables fast query performance for the slicer and visual-level filters, as Power BI can leverage the aggregate table via its aggregation feature, which automatically redirects queries to the pre-summarized data when possible.

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Disable the auto-date/time feature.

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Increase the data load frequency to every 15 minutes.

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Use DirectQuery mode to query the source database directly.

Question 10

Your Power BI model includes a calculated column that concatenates first and last name. Users report that the column shows blank for some rows. The data source has no nulls. What is the most likely cause?

Accepted Answer

One of the columns contains only spaces. If a source column contains only spaces (or empty strings that appear as spaces), concatenating it with another column can produce a result that appears blank or contains only spaces. Power BI's concatenation does not trim whitespace, so the resulting column may display as blank in visuals.

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Data type mismatch between the two columns

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The columns are from different tables without a relationship

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The relationship between tables is set to single direction

Question 11

You are a Power BI administrator for a large enterprise. You have a Power BI semantic model that uses a single large fact table named Sales (100 million rows) and several dimension tables. The model is used by multiple departments, each with different row-level security (RLS) rules based on the SalesRegion column. You have implemented RLS using static roles. However, you notice that when users from different departments view the same report page, the query performance varies significantly. You suspect that the RLS filters are causing the performance difference. You need to investigate and optimize the RLS performance. What should you do first?

Accepted Answer

Use Power BI Performance Analyzer to capture query performance for each user role and analyze the generated DAX queries in DAX Studio.. Performance Analyzer in Power BI captures the DAX queries generated for each user role and their durations, and exporting to DAX Studio allows deep analysis of query plans and RLS filter impact. This is the correct first step to diagnose why RLS causes performance variance across roles before making changes.

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Increase the data model's memory limit in Premium capacity.

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Convert all RLS roles to use dynamic RLS with USERPRINCIPALNAME.

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Remove all RLS roles and implement security at the report level using bookmarks.

Question 12

Which THREE factors should you consider when designing a star schema in Power BI?

Accepted Answer

A separate date table should be created for time intelligence.. A separate date table is required for time intelligence functions in Power BI because DAX time intelligence functions (e.g., TOTALYTD, SAMEPERIODLASTYEAR) rely on a continuous, contiguous date range with no gaps. Power BI automatically marks a table as a date table only if it contains a complete set of dates from the earliest to the latest transaction, enabling functions like DATEADD and DATESBETWEEN to work correctly across all granularities.

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Use natural keys instead of surrogate keys in dimension tables.

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Use a snowflake schema to reduce data redundancy.

Question 13

Which TWO of the following are true about the Power BI composite model?

Accepted Answer

A composite model can combine DirectQuery and Import tables.. A composite model in Power BI allows mixing DirectQuery and Import tables within the same data model. This enables you to leverage the performance of in-memory Import storage for some tables while using DirectQuery to access large or real-time data sources without duplicating data.

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Composite models do not support many-to-many relationships.

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All tables in a composite model must use the same storage mode.

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Calculated tables are not supported in composite models.

Question 14

You have a Power BI report with a matrix visual showing sales by region and product category. You want to allow users to expand and collapse groups. Which feature should you enable?

Accepted Answer

Drill-down mode. Drill-down mode in a matrix visual allows users to expand and collapse hierarchical levels. When a hierarchy is present, enabling drill-down mode provides expand/collapse icons on row headers, letting users interactively show or hide subcategories. Option A (page tooltips) shows tooltip pages on hover, unrelated to expand/collapse. Option C (drill-through) navigates to another report page, not within the same visual. Option D (row and column subtotals) is a formatting feature for aggregating totals, not for expanding/collapsing groups.

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Page tooltips

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Drill-through

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Row and column subtotals

Question 15

You are merging two tables in Power Query: Orders and Customers. The Orders table has a CustomerID column, and the Customers table has a CustomerID column. You want to keep all rows from Orders and only matching rows from Customers. Which join kind should you use?

Accepted Answer

Left Outer. (Left Outer) is correct because it returns all rows from the Orders table regardless of whether a matching CustomerID exists in the Customers table. When no match is found, the Customers columns will contain null values. This is the standard behavior for a left outer join in Power Query, which preserves the left table's rows entirely.

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Full Outer

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Inner

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Right Outer