CCNA Prepare the data Questions

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Option B is wrong because enabling the auto date/time option creates hidden date tables that are not user-visible and cannot be marked as a date table, which prevents proper use of time intelligence functions like TOTALYTD. Option C is wrong because using the OrderDate column directly in time intelligence measures will fail or produce incorrect results, as these functions require a separate, continuous date table with no gaps. Option D is wrong because creating a calculated column for fiscal year does not provide the continuous date table needed for time intelligence; it only adds a column without enabling functions like TOTALYTD.

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Option A is wrong because row-level security (RLS) in Power BI restricts which rows a user can see, not which columns are obfuscated; it cannot mask or hide specific column values. Option B is wrong because applying data masking in Power Query using Text.Start or Text.End functions would require manual transformation logic and would not leverage the existing SQL Server dynamic data masking, which is the intended and more secure approach. Option C is wrong because installing the on-premises data gateway is necessary for connecting to an on-premises SQL Server, but it does not affect whether dynamic data masking applies; the masking behavior depends solely on the database user's permissions, not the gateway configuration.

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Option B is wrong because the 'version' value in a JSON policy for Power BI data sources is typically '1.0', not '2.0'; '2.0' is used for other policy types like data masking, not for connection policies. Option C is wrong because the JSON policy does not require a 'dataSource' type; the data source type is defined in the connection string or the gateway configuration, not as a separate JSON field. Option D is wrong because the connection string in the JSON policy already includes the database name (e.g., 'Initial Catalog=AdventureWorks'), so a missing database name is not the issue; the failure is due to authentication permissions, not connection string syntax.

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Option A is wrong because importing all 100 million rows and then removing columns still loads the full row count into the data model, wasting memory and bandwidth; column removal does not reduce row volume. Option C is wrong because loading all data and applying a visual-level filter only hides rows in the report, but the entire dataset remains in the model, causing unnecessary memory consumption and slower performance. Option D is wrong because a calculated table in DAX still requires the full table to be loaded first before filtering, negating any data reduction at the import stage.

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Option B is wrong because the 'Parse' -> 'Date' transformation with a specific culture assumes all dates in the column follow that single culture's format; it cannot handle mixed formats like MM/dd/yyyy and dd/MM/yyyy in the same column. Option C is wrong because 'Split Column' by delimiter separates date parts into individual columns (e.g., day, month, year), but it does not resolve which part is day vs. month when the order is inconsistent, requiring additional logic to recombine correctly. Option D is wrong because 'Detect Data Type' only identifies the overall data type (e.g., text or date) and cannot distinguish between multiple date formats within the same column; it would likely fail or produce errors for rows not matching the dominant format.

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Option A is wrong because replacing the view with a physical table does not address the stale data issue; the dataset would still import data on the daily schedule, and the physical table would need its own refresh mechanism. Option B is wrong because increasing the scheduled refresh frequency to every 30 minutes changes the refresh schedule, which the question explicitly prohibits. Option C is wrong because incremental refresh still relies on the import storage mode and scheduled refresh; it only partitions data loading, not the refresh frequency, so the dataset would still only update at 2:00 AM daily.

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Option A is wrong because the try...otherwise block does catch parsing errors; the issue is that the conversion itself fails, not that the error handling is faulty. Option B is wrong because Text.Clean removes non-printable characters (like line feeds), not decimal separators; decimal separators are preserved. Option C is wrong because the function is applied to a text column (as stated), and the data type mismatch is not the root cause—the problem is the uncleaned text content, not the column type.

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Option A is wrong because setting the data type to Date only ensures the column is recognized as a date value, but it does not designate the table as a date table; time intelligence functions require a marked date table with a continuous range of dates. Option B is wrong because DATEADD is a time intelligence function used to shift dates, not a function to create a date table; creating a date table requires CALENDAR or CALENDARAUTO, not DATEADD. Option D is wrong because DATEVALUE converts a text string to a date, but it does not mark the table as a date table; the table must be explicitly marked in the model view for time intelligence to function properly.

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Option A is wrong because Query Folding is a Power Query optimization that pushes transformations back to the source, but it is already inherently enabled in DirectQuery mode (all transformations are folded to Snowflake); enabling it further does not improve performance. Option B is wrong because changing the dataset to Import mode would require modifying the data source (by storing data in Power BI), which violates the constraint of not changing the data source. Option C is wrong because increasing concurrent queries in Power BI Service does not improve individual query performance; it only allows more simultaneous users, potentially worsening contention and latency.

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Option B is wrong because splitting the column by delimiter would separate the value into multiple columns (e.g., '$1' and '234.56'), losing the original numeric integrity and requiring additional merging steps, which is inefficient and error-prone. Option C is wrong because changing the data type directly to Decimal Number will cause an error or return null for rows containing non-numeric characters like '$' and ','; Power Query's type conversion expects a clean numeric string. Option D is wrong because removing only commas leaves the '$' symbol intact, which still prevents Power Query from interpreting the value as a number, resulting in a conversion error.

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Option A is wrong because loading the column as text and ignoring conversion errors would leave the data as strings, preventing any time intelligence functions (e.g., DATEADD, DATESYTD) from working correctly, as they require a date or datetime data type. Option C is wrong because changing the column type directly to 'Date' would truncate the time portion of the datetime values, losing granularity needed for time-based calculations like hour-level trends. Option D is wrong because splitting the column into separate date and time columns is unnecessary and adds complexity; Power Query can handle mixed datetime formats natively with type detection, and splitting would require recombining for time intelligence, which is inefficient.

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Option A is wrong because the Excel Workbook connector is used for importing data from a single Excel file, not for combining multiple CSV files from a folder. Option B is wrong because the CSV connector imports only one CSV file at a time; it does not support batch processing or automatic combination of multiple files from a directory. Option C is wrong because the Web connector is designed to import data from web URLs or APIs, not from local or network folders containing CSV files.

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Option A is wrong because the date filter syntax is validated during the query execution in Power Query Editor, and since it runs successfully there, the syntax is correct. Option B is wrong because a syntax error in the let expression would cause the query to fail immediately in the editor, not only during 'Close & Apply'. Option D is wrong because if the column 'OrderDetails' did not exist, the query would fail in the editor with a 'column not found' error, not during the load phase.

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Option A is wrong because merging fact tables would create a single, denormalized table that violates star schema best practices, leads to data redundancy, and makes it harder to maintain separate business processes. Option B is wrong because calculated columns in the dimension table would create static values that do not dynamically respect filter context from the fact tables, and they cannot replace the functionality of active relationships in DAX measures. Option D is wrong because changing both relationships to active would create ambiguity in filter propagation, as Power BI cannot have two active one-to-many relationships from the same dimension table to different fact tables without causing cross-filtering issues.

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Option B is wrong because setting the fact table to DirectQuery avoids importing data into the in-memory model, but it does not minimize the model size—it shifts query execution to the source, which can degrade performance and is not a size-minimization technique for an imported model. Option C is wrong because enabling 'Include relationship columns' actually adds hidden columns to the fact table for relationship propagation, increasing model size rather than reducing it. Option D is wrong because creating a calculated column for row number adds a new column to the model, consuming additional memory and increasing the model size, which is the opposite of the goal.

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Option A is wrong because replacing '/' with '-' merely changes the delimiter character; the values remain as text strings and are not converted to the Date data type. Option C is wrong because splitting the column by delimiter '/' would separate the month, day, and year into three separate text columns, losing the original date structure and requiring additional steps to reassemble. Option D is wrong because promoting the first row as headers only affects column naming, not the data type of the values; it does not address the text-to-date conversion issue.

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Option B is wrong because the Q&A feature is a natural language query tool for exploring data, not a mechanism to exclude columns from the dataset. Option C is wrong because report-level filters only hide data at the visual or page level; the columns remain in the dataset and still consume memory. Option D is wrong because hiding columns in the model view only removes them from the field list in reports; they are still loaded into the data model and occupy space in the VertiPaq engine.

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Option A is wrong because connecting to each CSV file individually and using Append Queries is inefficient and error-prone; it requires manual setup for each file and does not scale well if new files are added to the folder. Option C is wrong because Merge Queries are used to join tables based on a common column (like a SQL JOIN), not to combine rows from multiple files with the same structure; this would incorrectly attempt to match rows across files rather than stacking them. Option D is wrong because changing data source settings to treat the folder as a single data source is not a valid Power Query operation; the folder connector already treats the folder as a container, but you must explicitly use the 'Combine Files' transform to merge the contents.

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Option A is wrong because DATESMTD returns dates from the start of the current month to the last date in the filter context, which gives month-to-date sales, not the last complete month. Option B is wrong because it hardcodes a specific date (January 1, 2023) rather than dynamically calculating the last complete month, so it will not adapt to different time periods. Option C is wrong because TOTALMTD is a deprecated function that calculates month-to-date totals, not the previous complete month, and its use is discouraged in modern DAX.

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Option A is wrong because 'Group By' aggregates rows based on a column, but it does not remove or replace null values; it would group nulls together but leave them in the data. Option B is wrong because 'Remove Duplicates' eliminates duplicate rows based on selected columns, but it does not address null values; nulls are considered duplicates of each other, but the transformation does not remove nulls unless the entire row is a duplicate. Option D is wrong because 'Replace Values -> Replace null with a default value' is actually the correct transformation to ensure no nulls in the ProductID column, but the question's answer key marks C as correct, indicating a potential misinterpretation or that the exam expects removal of rows with null ProductID rather than replacement.

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Option A is wrong because Merge Queries performs a join operation based on matching columns, which combines columns from different tables rather than stacking rows; this would not produce a single table of all region data. Option C is wrong because manually copying and pasting data from each sheet into a master sheet in Excel is inefficient, error-prone, and does not leverage Power Query's automated data transformation capabilities, which is the expected approach for the PL-300 exam. Option D is wrong because Group By is used to aggregate data by grouping rows based on column values and performing calculations like sum or count; it does not combine multiple sheets into one table.

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Option B is wrong because the Pivot Column feature transforms unique values from a column into multiple columns (pivoting), which does not flatten nested arrays into rows; it aggregates or spreads data horizontally, not vertically. Option C is wrong because Merge Queries combines two tables based on a matching key, but it does not expand nested arrays within a single column; it would require an existing separate table to join against. Option D is wrong because Group By aggregates rows based on grouping columns, often using sum, count, or other aggregations, but it cannot expand nested arrays into separate rows; it collapses data rather than expanding it.

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Option A is wrong because the SharePoint Online List connector is designed for SharePoint lists (e.g., custom lists, task lists), not for document libraries containing Excel files; it cannot read Excel workbook sheets. Option C is wrong because the Excel connector connects to a single Excel file, not a folder of workbooks, so it cannot combine data from multiple files. Option D is wrong because the Web connector is for connecting to web pages or APIs via HTTP, not for accessing SharePoint folder structures or parsing Excel files.

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Option A is wrong because ADDCOLUMNS is a fully supported DAX function that adds calculated columns to a table; it is not the cause of a slicer showing only one year. Option B is wrong because the CALENDAR function syntax is correct (e.g., CALENDAR(DATE(2020,1,1), DATE(2025,12,31))) and would generate a continuous date range; syntax errors would prevent the table from being created entirely, not cause partial data display. Option D is wrong because having too many rows (e.g., from 2020 to 2025) would not cause the slicer to show only 2024; it would show all years in the range.

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Option B (Full Outer join) is wrong because it returns all rows from both tables, including rows without matches, which would include Orders rows with no matching CustomerID. Option C (Right Outer join) is wrong because it returns all rows from Customers and only matching rows from Orders, which would include Customers rows without matching Orders, not the desired behavior. Option D (Left Outer join) is wrong because it returns all rows from Orders and only matching rows from Customers, which would include Orders rows with no matching CustomerID, contrary to the requirement.

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Option B is wrong because `List.Generate` is a general-purpose generator that requires a custom loop function with initial, condition, next, and optional transform parameters; it is overly complex and not the direct function for generating a simple sequence of dates. Option C is wrong because `List.DateTimes` generates a list of datetime values (including time components), not just dates, which would introduce unnecessary time granularity and potential performance overhead for a date table. Option D is wrong because `List.Range` extracts a contiguous subset from an existing list; it does not generate a new list of dates from scratch.

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Option A is wrong because `VALUES(Sales[Date])` returns only the distinct dates that actually exist in the Sales table, which may have gaps (e.g., weekends or holidays with no sales) and does not create a continuous date table required for time intelligence. Option B is wrong because `CALENDAR(MIN(Sales[Date]), MAX(Sales[Date]))` creates a contiguous date range but requires explicit MIN/MAX references, making it less elegant than `CALENDARAUTO()` and prone to error if the Sales table is filtered or empty. Option D is wrong because `DISTINCT(Sales[Date])` behaves identically to `VALUES` in this context—it returns only the existing dates without filling gaps, so it fails to produce a proper date dimension.

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Option A is wrong because adding a calculated column in Sales to extract fiscal year without creating a relationship means you cannot use the Date table as a filter; you would have to filter directly on the Sales table, losing the benefits of a star schema and the ability to use date table hierarchies. Option B is wrong because merging the Date table into the Sales table in Power Query would denormalize the data, creating a single flat table that violates star schema principles, increases data duplication, and makes time intelligence calculations difficult. Option D is wrong because a many-to-many relationship using a bridge table is unnecessary and overly complex for this scenario; the cardinality is clearly one-to-many (one fiscal year in Date to many orders in Sales), and a bridge table would introduce ambiguity and performance overhead without any benefit.

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Option B is wrong because 'Remove Errors' deletes entire rows containing any error, which can lead to data loss if other columns in those rows contain valid data. Option C is wrong because 'Change data type to Date and ignore errors' is not a valid Power Query operation; ignoring errors during type conversion still results in errors in the column, and there is no built-in 'ignore errors' toggle. Option D is wrong because filtering to exclude rows with invalid dates after type conversion requires the errors to be present first, and filtering on error values is not straightforward; it is more efficient to replace errors with null and then filter if needed.

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Option A is wrong because the SQL query uses `'>'` with a string, but SQL Server implicitly converts the string to a date for comparison, so the query will not fail. Option B is wrong while using a parameter is a best practice for maintainability, it does not directly address the performance issue of data volume; the question asks about a potential performance issue, not code quality. Option D is wrong because grouping in SQL could reduce data volume, but the primary performance bottleneck here is the row filter on `OrderQty > 10` being applied after data transfer; grouping after filtering is less impactful than filtering earlier.

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Option B is wrong because loading all 50 columns and hiding them still stores the full dataset in the model, wasting memory and slowing refresh times. Option C is wrong because the question specifies a CSV file, which does not support SQL queries; this option only applies to database sources like SQL Server. Option D is wrong because creating a DAX calculated table still loads the full dataset into the model first, then creates a subset in memory, which is less efficient than filtering at the import stage.

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Option A is wrong because changing the storage mode to DirectQuery would not improve refresh performance; it would shift query execution to the source database, but the underlying latency from the different region would still affect each query, and DirectQuery is not a refresh optimization. Option B is wrong because increasing the scheduled refresh frequency does not reduce the amount of data loaded each time; it simply runs the same full refresh more often, which could worsen performance by increasing load on the remote database. Option C is wrong because installing an on-premises data gateway is used to connect to on-premises data sources securely, but it does not reduce network latency between regions; the gateway itself adds a hop and does not address the geographic distance to the SQL Server.

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Option A is wrong because creating a composite key does not address the requirement of having unique values in at least one table for the relationship; it only combines multiple columns to form a unique identifier, but the underlying duplicate values in 'CustomerID' would still prevent a direct relationship on that column. Option B is wrong because merging queries combines tables into a single table, which eliminates the need for a relationship but is not the correct approach when you need to maintain separate tables and create a relationship between them; it also does not resolve the uniqueness requirement for the relationship. Option C is wrong because changing the data type of 'CustomerID' to text does not remove duplicate values; it only changes the data format, and duplicates would still exist, preventing the relationship from being created.

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Option A is wrong because creating a calculated table using VALUES from the Sales table's date column only includes dates where sales occurred, which may omit dates with no transactions, breaking time intelligence functions like TOTALYTD or SAMEPERIODLASTYEAR. Option B is wrong because using the Sales table's date column directly as the date dimension violates the star schema principle of having a separate, dedicated date table, leading to poor performance and inability to mark it as a date table for built-in time intelligence. Option C is wrong because enabling the auto date/time option in Power BI settings creates hidden date tables automatically, but these are not user-controllable, cannot be extended to a custom range like 2020–2025, and can cause confusion in model relationships.

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Option A is wrong because the Dense parameter in RANKX does not force all ranks to 1; it controls whether ranking gaps are skipped (e.g., 1,2,3 instead of 1,3,5) but does not affect the underlying values being ranked. Option B is wrong because the DESC parameter is correctly spelled as 'DESC' or 'DESCENDING' in DAX; there is no requirement for lowercase 'Desc', and a misspelling would cause a syntax error, not all ranks being 1. Option D is wrong because RANKX can be used in a calculated column; it does not require a measure, though the expression must be properly written to avoid row context issues.

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Option A is wrong because grouping by CustomerID and aggregating all columns would collapse multiple rows into one, losing granular detail and potentially discarding valid duplicate records that represent different customers or transactions. Option B is wrong because Power Query does not have a 'Merge with duplicates' option; the Merge Queries dialog only offers join kinds (e.g., Left Outer, Inner) and does not automatically handle duplicates—duplicates must be addressed before or after the merge. Option C is wrong because loading both tables as-is and creating a relationship in the data model would create a many-to-many relationship (due to duplicate CustomerIDs in the CSV), which Power BI does not support directly and would lead to ambiguous aggregations or errors.

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Option A is wrong because changing regional settings in Power BI Desktop affects all date interpretations globally, which can introduce errors in other parts of the report and is not a targeted fix for a single column. Option B is wrong because 'Replace Values' performs a simple text substitution without understanding date components, so swapping day and month would require complex logic and could corrupt data if values are not consistently formatted. Option D is wrong because splitting the column by delimiter and recombining is a manual, error-prone workaround that does not leverage Power Query's built-in date parsing capabilities and can introduce formatting inconsistencies.

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Option A is wrong because changing the delimiter to a semicolon does not address inconsistent column names or data types; it only changes how columns are parsed, which is irrelevant to schema merging. Option B is wrong because Table.RemoveRows removes rows that cause errors, but it does not fix the underlying issue of mismatched column structures; it merely discards data, potentially losing valid rows. Option C is wrong because adding a custom column to each file standardizes data types only for that column, not for all columns, and it requires manual effort per file, which is inefficient and does not handle variations in column names.

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Option A is wrong because loading each sheet as a separate query and then using 'Append Queries' requires manual effort for each sheet, which is inefficient and not scalable for many sheets. Option B is wrong because 'Merge Queries' is designed for joining tables based on a common column (like a SQL JOIN), not for stacking rows from multiple sheets; it would produce a wide table instead of a long one. Option D is wrong because creating a new query that references each sheet query and then merging them still involves manual referencing and is less efficient than the built-in 'Combine Files' functionality, which automates the process.

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Option A is wrong because an Inner join would only return orders that have a matching customer, discarding any orders with missing or unmatched CustomerID values, which does not satisfy the requirement to add CustomerName to all orders. Option C is wrong because a Right Outer join would return all rows from the Customers table, which is not the target table; it would keep all customers even if they have no orders, and orders without a matching customer would be lost. Option D is wrong because a Full Outer join returns all rows from both tables, creating nulls on both sides for non-matches, which is unnecessary and would introduce extra rows for customers with no orders, bloating the result.

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Option A is wrong because importing each CSV file separately and then appending them is inefficient and error-prone, requiring manual steps for each file and breaking the automated refresh capability. Option C is wrong because writing a Python script in Power Query is not natively supported; Power Query uses M language, and Python integration requires additional configuration (e.g., Python in Power BI Desktop) and is not the most efficient or standard approach for this task. Option D is wrong because using a dataflow is an overkill for a simple folder import; dataflows are designed for complex ETL processes and cloud-based transformations, not for directly loading local CSV files into a single table in Power BI Desktop.

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Option B is wrong because 'Merge Columns' is used to combine multiple columns into one, not to split a single column. Option C is wrong because 'Extract Text' allows you to pull out substrings based on position or length (e.g., first N characters), but it cannot dynamically split on a delimiter like a space. Option D is wrong because 'Replace Values' is designed to substitute one text value with another, not to separate a column into multiple parts.

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Option B is wrong because creating dataflows with certified sources does not enforce that all datasets across the organization use certified sources; it only provides a recommended approach but does not block uncertified sources. Option C is wrong because sensitivity labels in Purview control data classification and protection, not data source certification enforcement; they are unrelated to the requirement for certified sources. Option D is wrong because restricting dataset creation to administrators does not enforce the use of certified data sources; it only limits who can create datasets, not what sources they can use.

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Option A is wrong because scheduling a daily refresh in the Power BI service only applies to Import mode datasets; DirectQuery mode does not cache data, so scheduled refresh does not update the schema. Option B is wrong because creating a new measure referencing the column is impossible if the column is not yet recognized in the model; the column must first be available in the field list. Option C is wrong because publishing the report again merely uploads the current .pbix file; if the column was not added in Power BI Desktop before publishing, it will still be missing in the service.

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Option A is wrong because 'Replace Errors' is used to replace error values (e.g., from data type conversion failures), not NULL values; NULLs are not errors. Option B is wrong because 'Fill Down' propagates the previous non-null value downward, which is not appropriate for replacing NULLs with a constant 0 and can introduce incorrect data patterns. Option D is wrong because 'Change Type' to a non-nullable type will cause the query to fail or produce errors when encountering NULLs, rather than replacing them with 0.

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Option B is wrong because Merge Queries performs a join based on matching columns (like SQL JOINs), which combines columns horizontally rather than appending rows vertically, and would require a key column to match records. Option C is wrong because Group By aggregates data by grouping rows based on a column and calculating summaries (e.g., sum, count), which does not add rows from another table. Option D is wrong because Pivot Column transforms unique values from a column into new columns, reshaping data from rows to columns, which is the opposite of appending rows.

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Option A is wrong because using a local folder with a personal gateway requires the gateway to be running on a machine with access to the folder, which adds complexity and is not fully automated for a weekly export; also, new campaigns would be included only if the folder path is dynamic, but the personal gateway introduces a dependency. Option B is wrong because manually re-importing the CSV each week defeats the automation requirement and is not scalable for weekly updates. Option C is wrong because incremental refresh requires a date/time field with a defined range and a supporting policy in Power BI Service, and it does not automatically handle new campaigns unless the data source itself supports partitioning; it also requires a more advanced setup than a simple CSV import.

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Option B is wrong because Power Query does not automatically convert the column to text; the transformation explicitly changes the column type to a number, and if it fails, it produces errors, not a text conversion. Option C is wrong because the comma acts as a non-numeric character in the default locale, preventing successful conversion to numbers without prior data cleaning (e.g., replacing commas with empty strings). Option D is wrong because Power Query does not ignore the comma; it strictly parses the value and fails when encountering an unrecognized character, unlike some other tools that might auto-detect locale settings.

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Option B is wrong because increasing the scheduled refresh frequency only updates the data more often; it does not improve query performance against the existing imported data. Option C is wrong because reducing the number of dimension tables would break the star schema design, potentially causing data redundancy and incorrect relationships, and it does not directly address query speed. Option D is wrong because enabling 'Load to report' for all tables simply makes them available in the Power BI model; it has no impact on query performance and may even increase memory usage.

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Option B is wrong because using VAR and FILTER with ALL to compute a running total is less efficient than time intelligence functions, as it requires scanning the entire table in row context and can degrade performance on large datasets, and it does not leverage Power BI's optimized time intelligence engine. Option C is wrong because creating calculated columns for Year and Month increases model size and storage overhead, and using SUMX with FILTER in a measure is less efficient than a dedicated time intelligence function like TOTALYTD. Option D is wrong because using EARLIER in a calculated column to compute a running total is highly inefficient, as it iterates over rows in a nested context, leading to poor performance and larger model size due to materialized columns.

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Option B is wrong because 'Merge Queries' is used to combine two separate tables based on matching keys, not to flatten nested arrays within a single JSON structure. Option C is wrong because 'Pivot Column' transforms unique values from a column into new columns, which would not flatten nested arrays but rather spread data horizontally, creating a wider table. Option D is wrong because 'Unpivot Columns' is used to convert columns into rows (e.g., for normalization), but it operates on record columns, not list columns, and cannot directly expand nested JSON arrays.

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Option A is wrong because relying on auto date/time creates hidden date tables that are not user-defined, cannot be marked as a date table, and may not cover the full contiguous range needed for custom time intelligence. Option B is wrong because adding calculated columns for year and month does not address gaps in the date range; it only extracts parts from existing dates, leaving missing dates unhandled. Option D is wrong because using the existing Date column as the date table without ensuring contiguity can lead to gaps in the date dimension, causing time intelligence functions to return incorrect results or errors.

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Option A is wrong because an Inner Join returns only rows where there is a match in both tables, which would exclude orders without returns. Option B is wrong because a Right Outer Join returns all rows from the right table ('Returns') and only matching rows from the left table ('Orders'), which would include all returns but not all orders. Option C is wrong because a Full Outer Join returns all rows from both tables, including non-matching rows from both sides, which would include returns without orders and is not the requirement.

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Option A is wrong because Power Query can fold comparisons between a datetime column and an integer filter by implicitly converting the integer to a datetime (e.g., interpreting 2020 as a year), so this would not prevent folding. Option B is wrong because using a native SQL query does not inherently prevent folding; in fact, native SQL queries can still be folded if the source supports it, though they may limit further folding steps. Option D is wrong because DirectQuery mode does not prevent folding; in DirectQuery, all query transformations are sent to the source as SQL, so folding is actually maximized, not prevented.