PL-300 Practice Test 33 — 15 Questions

Question 1

Which THREE considerations are important when designing a Power BI data model for large datasets?

Accepted Answer

Disable the auto-date/time feature.. Option C is correct because Power BI's Auto date/time feature creates a hidden calculated date table for every date column, which adds memory overhead and can bloat a large model, so disabling it (via Options or Tabular Editor) keeps the model lean. Option D is correct because a star schema with a central fact table surrounded by dimension tables produces efficient, simple relationships that the VertiPaq engine compresses and queries faster than snowflake or normalized designs. Option E is correct because integer keys consume less memory and compress better than text keys, and integer-to-integer relationships are processed more efficiently during query execution. Option A is not appropriate because calculated columns are stored and compressed in the model, consuming memory and increasing refresh time, so measures or calculated tables should be preferred where possible. Option B is not appropriate because adding unnecessary columns increases model size, hurts compression, and slows processing, so fact tables should contain only the columns needed for analysis.

Answer

Store calculated columns in the fact table for quick access.

Answer

Include as many columns as possible in fact tables for flexibility.

Question 2

You are importing data from a folder containing multiple CSV files with identical structure. You use the 'Combine files' transform in Power Query. Which TWO statements are true about this process?

Accepted Answer

Power Query creates a function that applies the same transformations to each file.. Option D is correct because when you use the 'Combine files' transform in Power Query, it generates a reusable function (typically named 'Transform File' or similar) that encapsulates the transformations applied to the sample file, and then invokes that function for every other file in the folder so each file undergoes the same steps. Option E is correct because Power Query creates a separate sample file query (often named 'Sample File' or 'Transform Sample File') that acts as a template, defining the schema and transformation logic that the function applies to all files. Option A is not correct because the sample file is not automatically chosen by alphabetical order; Power Query typically selects the first file it encounters (often based on the folder's file listing order) and lets the user pick a different sample file if needed. Option B is not correct because all files in the folder are combined, not just the sample file. Option C is not correct because the combine process is handled entirely within Power Query's M engine, not by Power Automate.

Answer

The sample file is automatically selected as the first file in alphabetical order.

Answer

Only the sample file is imported; other files are ignored.

Answer

The combine process uses Power Automate to merge files.

Question 3

You are developing a Power BI semantic model that uses a large fact table from Azure Synapse Analytics. You need to optimize the model for performance. Which THREE actions should you take?

Accepted Answer

Disable auto-date/time feature for the model.. Option A is correct because disabling the Auto date/time feature prevents Power BI from creating hidden date tables for every date column, which reduces model size and memory consumption and speeds up processing and refresh. Option B is correct because integer surrogate keys compress far better in the VertiPaq engine than string keys, lowering memory usage and improving join and relationship performance. Option E is correct because removing unnecessary columns from the fact table reduces the model's memory footprint and improves compression and query performance. Option C is not appropriate because combining the fact table with dimensions into a single wide table denormalizes the star schema, increases storage and redundancy, and typically degrades performance rather than optimizing it. Option D is not appropriate because calculated columns are computed at refresh time and consume memory, whereas transformations in Power Query are performed during load and are generally preferred for performance.

Answer

Combine the fact table with dimension tables into a single wide table.

Answer

Create calculated columns in the fact table instead of in Power Query.

Question 4

Which THREE factors should you consider when designing an incremental refresh policy for a large fact table in Power BI?

Accepted Answer

The refresh policy must consider the data warehouse's maintenance windows. Option C is correct because an incremental refresh policy must be scheduled so that its refresh windows do not collide with the data warehouse's maintenance windows, since the source must be available and consistent when Power BI queries it for new or changed partitions. Option D is correct because incremental refresh works by partitioning the table in the Power BI model (typically by RangeStart/RangeEnd parameters on a date column), so only the newest partitions are refreshed while historical partitions are retained. Option E is correct because incremental refresh requires a date or datetime column in the source table to filter and define the partition boundaries; without it, Power BI cannot determine which rows are new or changed. Option A is not required, since incremental refresh is supported for Import mode (and DirectQuery is not a prerequisite). Option B is not required, as the source can be an on-premises database accessed via a data gateway, not only a cloud database.

Answer

The table must be configured for DirectQuery mode

Answer

The source data must be stored in a cloud database

Question 5

Which THREE of the following are best practices for data modeling in Power BI? (Select exactly three.)

Accepted Answer

Create a separate date table for time intelligence functions.. Option C is correct because a dedicated, continuous date table marked as a date table is required for reliable time intelligence functions such as TOTALYTD, SAMEPERIODLASTYEAR, and DATEADD, and it avoids gaps or duplicate dates that break those calculations. Option D is correct because primary key columns in dimension tables are used only for relationship joins, so hiding them from report view keeps the field list clean and prevents report authors from accidentally dragging surrogate keys into visuals. Option E is correct because a star schema with a central fact table surrounded by dimension tables delivers optimal VertiPaq compression, simpler DAX, and faster query performance than snowflaked or flat designs. Option A is not a best practice because bi-directional cross-filtering on all tables creates ambiguous filter paths, hurts performance, and can produce incorrect results; it should be used sparingly and only for specific many-to-many or security scenarios. Option B is not a best practice because calculated columns are computed at refresh, consume memory, and cannot respond to slicer context, whereas measures are evaluated at query time and are the preferred way to store dynamic business logic.

Answer

Use bi-directional cross-filtering relationships for all tables.

Answer

Store calculated logic in calculated columns rather than measures when possible.

Question 6

Which THREE factors should you consider when choosing between Import and DirectQuery storage modes? (Choose three.)

Accepted Answer

Report interactivity and performance needs.. Option B is correct because Import mode loads data into the in-memory VertiPaq engine and typically delivers the fastest query performance and richest interactivity, while DirectQuery pushes queries to the source and can introduce latency, so the report's interactivity and performance needs directly drive the choice. Option D is correct because Import is bounded by dataset size limits (for example, 1 GB per dataset on Shared/Pro capacity, up to 400 GB on Premium/Fabric capacities) and memory, whereas DirectQuery has no such dataset size limit since data stays at the source, making data volume a key factor. Option E is correct because Import requires scheduled refreshes (up to 8 per day on Pro, 48 on Premium) and only shows data as of the last refresh, while DirectQuery reflects source changes in near real time, so refresh frequency and latency requirements are decisive. Option A is not a storage-mode factor because the Pro vs Premium license affects capacity features and refresh limits rather than the fundamental Import vs DirectQuery decision. Option C is irrelevant because color scheme and branding are purely visual design choices unrelated to how data is stored or queried.

Answer

User license type (Pro vs Premium).

Answer

Report color scheme and branding.

Question 7

Which TWO options are valid methods to combine multiple tables in Power Query?

Accepted Answer

Merge. In Power Query, Merge is the correct operation for combining two tables horizontally by matching rows on one or more key columns, equivalent to a SQL JOIN, and it is performed via Home > Merge Queries. Append is the correct operation for stacking tables vertically, adding the rows of one table beneath another, equivalent to SQL UNION ALL, and it is performed via Home > Append Queries. The other options are not Power Query commands: Join and Union are SQL terms (Union also implies deduplication, which Append does not do), and Concatenate is a text function (Text.Combine / & operator) for joining string values, not tables.

Answer

Join

Answer

Concatenate

Answer

Union

Question 8

You are profiling data in Power Query Editor. Which THREE tasks can you perform using the Column Profile feature?

Accepted Answer

Identify data type issues. The Column Profile feature in Power Query Editor is a read-only data profiling tool, so it can identify data type issues (B) by showing errors and mismatches in the column statistics, which helps you spot columns that need type correction. It also counts distinct values (C), displaying the number of unique entries in the column profile pane to assess cardinality. Additionally, it shows the distribution of values (D) through value frequency charts and statistics such as count, min, max, and unique counts, helping you understand data patterns. The unmarked options do not belong because adding a conditional column (A) and replacing values (E) are data transformation operations performed via the Add Column and Transform ribbons, not tasks provided by the profiling feature.

Answer

Add a conditional column

Answer

Replace values

Question 9

Which TWO are best practices when preparing data for Power BI?

Accepted Answer

Promote headers if the first row contains column names. Option B is correct because promoting headers when the first row contains column names ensures Power BI treats that row as field names rather than data, giving properly named columns for modeling and visuals. Option C is correct because splitting columns that contain multiple values into separate rows normalizes the data into a proper tabular shape, which is required for accurate aggregations and relationships in Power BI. Option A is not a best practice because data types should generally be set as early as possible, not after all transformations, to avoid errors and unexpected coercion. Option D is wrong because merging queries is a valid and often efficient technique; lookups are not always preferable. Option E is wrong because unpivoting is a recommended transformation to convert wide (crosstab) data into a tall, analysis-friendly structure.

Answer

Change data type of all columns after all transformations

Answer

Avoid merging queries; always use lookups

Answer

Avoid unpivoting columns; keep data wide

Question 10

Which THREE of the following are best practices when preparing data in Power BI for optimal performance?

Accepted Answer

Set correct data types for all columns.. Option C is correct because assigning the correct data types (for example, whole number, decimal, date/time, or text) to every column reduces memory usage in the VertiPaq engine, enables efficient compression, and prevents implicit conversions that slow down DAX calculations. Option D is correct because removing unnecessary columns and rows during import (ideally in Power Query before loading) shrinks the data model, lowers memory consumption, and speeds up refresh and query processing. Option E is correct because query folding translates Power Query transformations into native source queries (for example, SQL statements), letting the source database perform filtering, joins, and aggregations so less data is transferred and processed locally. Option A is not a best practice because merging all tables into one wide table creates redundancy, bloats the model, and destroys the star-schema relationships that Power BI optimizes for. Option B is not a best practice because calculated columns are computed and stored during refresh, consuming memory and increasing model size, whereas measures are evaluated at query time and are generally preferred for aggregations.

Answer

Merge all tables into a single table for simplicity.

Answer

Create calculated columns instead of measures when possible.

Question 11

Which THREE are types of Power Query transforms that can be used to clean data? (Choose three.)

Accepted Answer

Remove duplicates. Option A (Remove duplicates) is correct because Power Query provides a dedicated 'Remove Duplicates' transform that eliminates rows with identical values across selected columns, a core data-cleaning operation. Option C (Replace values) is correct because the 'Replace Values' transform substitutes specific text or numeric values (e.g., replacing 'N/A' with null), which is a standard cleansing step. Option E (Change data type) is correct because Power Query's 'Data Type' transform converts columns to the proper types (Text, Whole Number, Date, etc.), fixing type mismatches that would otherwise break calculations or loads. Option B (Group rows by a column) is not a cleaning transform but an aggregation/reshaping operation that summarizes data. Option D (Merge queries) is not a cleaning transform but a join operation that combines two queries, which is a data-shaping rather than cleansing activity.

Answer

Group rows by a column

Answer

Merge queries

Question 12

Which TWO are valid ways to combine data from multiple sources in Power Query? (Choose two.)

Accepted Answer

Append Queries.. Append Queries (B) is correct because it stacks rows from two or more tables with matching or similar columns into a single table, which is the standard Power Query way to combine data vertically from multiple sources. Merge Queries (D) is correct because it joins two tables horizontally on one or more matching key columns, equivalent to a SQL JOIN, allowing data from multiple sources to be combined side by side. Pivot Column (A) is not a combining operation; it reshapes existing values in a single table from rows into columns. Create relationships in the data model (C) links tables for analysis in Power Pivot/DAX but does not itself combine or transform data within Power Query. Group By (E) aggregates rows within a single table (e.g., sum, count) and does not combine data from multiple sources.

Answer

Pivot Column.

Answer

Create relationships in the data model.

Answer

Group By.

Question 13

Which TWO of the following are valid options when connecting to an on-premises SQL Server database from Power BI service? (Select TWO.)

Accepted Answer

DirectQuery mode with an on-premises data gateway. Option A (DirectQuery mode with an on-premises data gateway) is correct because the Power BI service cannot reach an on-premises SQL Server directly, so it must route live DirectQuery requests through an on-premises data gateway installed in the local network. Option D (Import mode with an on-premises data gateway) is correct because scheduled imports of on-premises SQL Server data into the Power BI service likewise require the on-premises data gateway to bridge the cloud service to the local database. Option B is wrong because a SharePoint Online list connector targets SharePoint lists, not SQL Server tables, and cannot serve as a SQL Server connection path. Option C is wrong because cloud credentials alone cannot traverse the corporate network boundary to reach an on-premises SQL Server without a gateway. Option E is wrong because scheduling refresh is a configuration action performed in the Power BI service (not a connection option), and Power BI Desktop itself does not run scheduled refreshes.

Answer

Using a SharePoint Online list connector pointing to SQL Server

Answer

DirectQuery using cloud credentials without a gateway

Answer

Scheduling refresh in Power BI Desktop

Question 14

Which TWO actions should you take to reduce the size of a Power BI dataset? (Choose two.)

Accepted Answer

Filter out rows that are not needed.. Option A is correct because filtering out rows that are not needed during import (for example, using Power Query filters or a WHERE clause in the source query) reduces the number of records loaded into the model, directly shrinking the dataset size in memory and on disk. Option B is correct because removing unnecessary columns during import eliminates entire columns of data from the model, which reduces both row-level storage and the columnar compression footprint in the VertiPaq engine. Option C is incorrect because disabling query folding typically hurts performance and does not reduce dataset size; folding pushes transformations back to the source. Option D is incorrect because adding calculated columns increases the model's size by storing additional materialized values. Option E is incorrect because DirectQuery does not store data in the model at all, but it is a connectivity mode change rather than a size-reduction action, and it does not reduce the size of an existing Import-mode dataset.

Answer

Disable query folding to improve performance.

Answer

Add calculated columns to precompute values.

Answer

Use DirectQuery instead of Import.

Question 15

Which TWO of the following are best practices for designing a star schema in Power BI?

Accepted Answer

Dimension tables should have a primary key and descriptive columns.. Option A is correct because in a star schema, dimension tables serve as the lookup/reference tables, so they must have a primary key that uniquely identifies each row (e.g., ProductKey, DateKey) plus descriptive attributes such as product name, category, or color used for slicing and grouping in Power BI visuals. Option C is correct because fact tables store the measurable events (sales, quantities, amounts) and must contain foreign keys that relate back to the primary keys of the dimension tables, forming the one-to-many relationships that Power BI's VertiPaq engine and DAX rely on for efficient filtering and aggregation. Option B is not a best practice because calculated columns in fact tables consume memory and storage, and business logic is better handled via measures or in the source/ETL layer to keep the fact table lean. Option D is wrong because flattening all tables into a single wide table defeats the star schema's purpose, causing redundancy, larger model size, and degraded performance. Option E is incorrect because many-to-many relationships between fact and dimension tables are an anti-pattern in star schema design; relationships should be one-to-many from dimension to fact to ensure correct filter propagation and predictable results.

Answer

Fact tables should contain calculated columns for business logic.

Answer

Merge all tables into a single flat table for simplicity.

Answer

Use many-to-many relationships between fact and dimension tables.