Workday Workday-Prism-Analytics Practice Test Questions Answers

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a Data Change task is used to load or update data into a table, which can involve importing data from multiple source files. According to the official Workday Prism Analytics study path documents, when loading rows from multiple source files into an empty table, the source files must share the same schema. The schema defines the structure of the data, including the column names, data types, and their order, which ensures that the data from all source files can be consistently mapped and loaded into the target table without errors.

The schema is critical because the Data Change task relies on a predefined table structure to process the incoming data. If the schemas of the source files differ (e.g., different column names or data types), the task will fail due to inconsistencies in data mapping. The other options are not required to be the same:

Source: The source files can originate from different systems or locations (e.g., Workday, external systems, or file uploads) as long as the schema aligns.

Naming convention: The names of the source files do not need to follow a specific convention for the Data Change task to process them.

Size: The size of the source files (e.g., number of rows or file size) can vary, as the task processes the data based on the schema, not the volume.

Thus, the requirement for the source files to have the same schema ensures seamless data loading into the table, maintaining data integrity and consistency during the transformation process.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, removing data from a Prism data source (PDS) without affecting dependent custom reports requires a careful approach to preserve the data source’s structure and dependencies. According to the official Workday Prism Analytics study path documents, the task to use is Delete Published Rows (option D). This task removes the data rows within the Prism data source while keeping the data source’s metadata (e.g., field definitions) and structure intact. Since custom reports depend on the data source’s structure rather than the specific data rows, deleting the published rows will not break the reports. After deleting the rows, you can republish the dataset with updated data, and the reports will continue to function with the new data, assuming the structure remains unchanged.

The other options are incorrect:

A. Inactivate Dataset: Inactivating a dataset disables it but does not remove data from the published data source, and it may still affect reports by making the data source inaccessible.

B. Delete Dataset: Deleting the dataset entirely will also delete the Prism data source, breaking any dependent custom reports.

C. Unpublish Dataset: Unpublishing the dataset removes the Prism data source, which will break dependent reports until the dataset is republished.

The Delete Published Rows task ensures that data is removed from the Prism data source without impacting the dependent custom reports, allowing for seamless data updates.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, restricting access to specific rows in a Prism data source (PDS) based on afield like Location Hierarchy for users such as Expense Partners requires implementing row-level security. According to the official Workday Prism Analytics study path documents, to meet the requirement that Expense Partners only see expense reports for the locations they support, you should run the Edit Data Source Security task to update row-level security and then republish the PDS (option B).

The Location Hierarchy (instance) field in the dataset can be used to define row-level security rules, ensuring that each Expense Partner only sees data for their assigned locations. The Edit Data Source Security task allows you to configure row-level security by mapping the Location Hierarchy field to the user’s security profile (e.g., their assigned locations). After updating the security settings, republishing the Prism data source is necessary to apply the changes, ensuring the updated security rules take effect in the reports that use the data source.

The other options are incorrect:

A. Run the Edit Data Source Security task to update the Securing Entities and then republish the PDS: Securing Entities are used to control overall access to the data source (e.g., via security domains), not to restrict data at the row level based on a field like Location Hierarchy.

C. Run the Edit Data Source Security task to update row-level security: While updating row-level security is correct, the changes will not take effect in reports until the data source is republished, making this option incomplete.

D. Run the Edit Data Source Security task to update the Securing Entities: As with option A, updating Securing Entities does not address row-level security for location-based restrictions.

Updating row-level security and republishing the PDS ensures that Expense Partners only see the relevant expense reports, meeting the requirement while maintaining security governance.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a matrix report in Workday is designed to display summarized data in a grid format, with built-in capabilities to calculate aggregations like minimum, maximum, total, average, and median. According to the official Workday Prism Analytics study path documents, to produce a Prism data source for a matrix report that needs to display the minimum, maximum, total, average, and median purchase order amount by location and month, you should publish your detail data and build the summarizations in the matrix report (option A).

Publishing the detail data (i.e., the raw purchase order data with fields like location, month, and amount) as a Prism data source allows the matrix report to access the granular data. The matrix report can then apply the required aggregations (MIN, MAX, SUM, AVG, MEDIAN) directly, grouping by location and month as specified in the report configuration. This approach leverages Workday’s reporting capabilities, reducing the need for additional transformations in Prism and ensuring flexibility for future reporting needs.

The other options are less efficient:

B. Add a Group By stage to your derived dataset: A Group By stage in the derived dataset can compute some aggregations (e.g., SUM, AVG), but Prism does not natively support calculating the median in a Group By stage, and it would require multiple stages or calculated fields to compute all metrics, making it less practical than using the matrix report.

C. Publish your detail data and build the summarizations in the advanced report: While an advanced report can perform some summarizations, it is not as well-suited as a matrix report for displaying multiple aggregations (like median) in a grid format by location and month.

D. Add two Group By stages to your derived dataset: Using two Group By stages is unnecessary and still does not address the limitation of calculating the median in Prism, making this approach overly complex.

Publishing the detail data and letting the matrix report handle the summarizations is the most efficient and effective way to meet the requirements.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, the Data Change task is used to load data from a source file into a table, and it supports flexible field type mapping to accommodate differences between source and target field types. According to the official Workday Prism Analytics study path documents, when the source file has a Numeric field type and the target table has a Text field type, you can map the Numeric field to the Text field in the Data Change task. Workday Prism Analytics automatically handles the conversion of Numeric values to Text during the data loading process, as Text fields can store Numeric values as strings without data loss.

The other options are not necessary or appropriate:

A. Use a different source file, as the field types are incompatible: The field types are not incompatible; Prism supports mapping Numeric to Text, making a new source file unnecessary.

C. Change the field type in the parsing stage from Numeric to Text: The parsing stage defines how the source file is interpreted, but changing the source field type is not required since the mapping handles the conversion.

D. Change the connection type for the data change task: The connection type (e.g., file upload or Workday report) is unrelated to field type compatibility and does not address the issue.

By mapping the Numeric field to the Text field in the Data Change task, the data can be successfully loaded, leveraging Prism’s built-in type conversion capabilities to ensure compatibility.