Workday-Prism-Analytics Sample Questions Answers

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, deleting published rows from a Prism data source (PDS) is a step taken to correct errors in the published data, often followed by republishing the dataset with corrected data. According to the official Workday Prism Analytics study path documents, when you delete the published rows, the report definition remains intact and will work after republishing (option A). The custom report’s definition, which is based on the Prism data source, is not affected by the deletion of published rows because the report definition references the data source’s structure (e.g., fields and metadata), not the specific data rows. Once the dataset is republished with the corrected data, the report will automatically reflect the updated data without requiring any changes to the report definition, assuming the structure of the data source remains the same.

The other options are incorrect:

B. The report definition will need to be manually recreated: The report definition is not deleted or invalidated by deleting published rows, so recreation is not necessary.

C. The report definition will be copied and a new version will appear after republishing: Workday does not automatically copy or version report definitions when a data source is republished.

D. The report definition will need to be edited to reflect changes: No edits are required unless the structure of the data source (e.g., field names or types) changes, which is not indicated in this scenario.

The report definition’s integrity is maintained, and it will function as expected after republishing the corrected data.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a multi-instance field contains multiple values for a single record (e.g., a list of skills for a worker). To convert each instance of a multi-instance field into a single-instance field, you need a transformation that expands the data into multiple rows, with each row containing one instance. According to the official Workday Prism Analytics study path documents, the Explode stage (option B) is the transformation stage designed for this purpose. The Explode stage takes a multi-instance field and creates a new row for each instance, transforming the multi-instance field into a single-instance field in the output. For example, if a worker has three skills in a multi-instance field, the Explode stage will create three rows, each with a single skill value in a single-instance field.

The other options are incorrect:

A. Unpivot: Unpivot transforms columns into rows (e.g., converting wide data to long format), but it does not handle multi-instance fields, which are a specific Workday data type.

C. Manage Fields: The Manage Fields stage modifies field properties (e.g., type, name) but cannot expand a multi-instance field into multiple rows.

D. Group By: The Group By stage aggregates data (e.g., summing values by a key) but does not convert multi-instance fields into single-instance fields.

The Explode stage is the correct transformation to achieve the conversion of a multi-instance field into a single-instance field by expanding the data into multiple rows.

Comprehensive and Detailed Explanation From Exact Extract:

When importing data into Workday Prism Analytics from a Workday report, including Workday instance field types in the report is critical for enabling specific functionality in the resulting Prism data source. According to the official Workday Prism Analytics study path documents, including instance field types allows the final Prism data source to support drilling into Workday objects. Instance field types represent references to Workday business objects (e.g., Worker, Position, or Organization), and including them in the report ensures that the Prism data source retains the ability to navigate to these objects within Workday’s reporting and analytics framework. This enables users to perform drill-down actions, such as accessing detailed object data directly from Prism visualizations or reports.

The other options do not accurately reflect the primary benefit of including instance field types:

B. Performance is improved in the final Prism datasource when published: Instance field types do not directly impact the performance of the published data source; performance is more influenced by data volume and indexing.

C. Unions are more easily performed with instance field types: Unions depend on schema compatibility, not instance field types, which are specific to Workday object references.

D. Joins are more easily performed with instance field types: While instance field types can be used in joins, their primary purpose is to enable object navigation, not to simplify join operations.

By including instance field types, the Prism data source gains enhanced interactivity, allowing users to leverage Workday’s object model for deeper analysis and navigation.

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, window functions are used to perform calculations across a set of rows, such as calculating averages with a function like AVG. According to the official Workday Prism Analytics study path documents, the AVG window function, which computes the average, must operate on a fieldof type Numeric. This is because averaging requires numerical values to perform arithmetic operations (e.g., summing the values and dividing by the count of rows). Non-numeric field types, such as Text or Date, cannot be averaged, and Boolean fields (true/false) are not suitable for this type of calculation. For example, a window function like AVG(salary) OVER (PARTITION BY department) would calculate the average salary per department, where "salary" must be a Numeric field.

The other options are incorrect:

A. Text: Text fields cannot be used for arithmetic operations like averaging.

B. Boolean: Boolean fields (true/false) are not suitable for calculating averages.

D. Date: Date fields cannot be directly averaged; they require conversion to a numeric representation (e.g., days since a reference date) first.

The requirement for a Numeric field type ensures that the AVG window function can perform the necessary mathematical computations accurately.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, creating a Prism data source that supports immediate incremental updates as data is updated in Prism requires a specific configuration. According to the official Workday Prism Analytics study path documents, the administrator should create a table and select the Enable for Analysis checkbox (option A). The "Enable for Analysis" option, when selected during table creation, allows the table to be used directly as a Prism data source with real-time updates. This setting ensures that as data in the table is updated (e.g., through a Data Change task), the changes are immediately reflected in the Prism data source, enabling incremental updates without the need for republishing. This is particularly useful for scenarios requiring near-real-time data availability in reporting or analytics.

The other options do not achieve the goal of immediate incremental updates:

B. Create a table and select Publish: Publishing a table creates a static Prism data source, but updates to the table require republishing, which does not support immediate incremental updates.

C. Publish a derived dataset with the Prism: Default to Dataset Access Domain: Publishing a derived dataset creates a data source, but updates to the underlying data require republishing the dataset, which is not concurrent or incremental.

D. Set Data Source Security on a derived dataset and select Publish: Setting security and publishing a derived dataset follows the same process as option C, requiring republishing for updates, which does not meet the requirement for immediate updates.

Selecting the "Enable for Analysis" checkbox when creating a table ensures the Prism data source supports concurrent, incremental updates as data changes in Prism.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a Stage Alert in the View Dataset Lineage report indicates an issue with the dataset’s transformation pipeline, such as a disconnected pipeline resulting from changing the imported pipeline in a Join stage. According to the official Workday Prism Analytics study path documents, a disconnected pipeline occurs when a pipeline (e.g., a table or dataset) is no longer referenced by any transformation stage, often after modifying the Join stage to use a different imported pipeline. To resolve this alert, the recommended action is to delete the disconnected pipeline (option D). By removing the disconnected pipeline from the dataset, the lineage is updated to reflect only the active pipelines, and the Stage Alert will disappear, indicating that the dataset’s configuration is now valid.

The other options are not appropriate:

A. Add a Manage Fields stage and re-attach the pipeline: A Manage Fields stage modifies field properties and cannot re-attach a disconnected pipeline to the Join stage.

B. Publish the derived dataset: Publishing the dataset does not resolve the issue of a disconnected pipeline; the alert will persist until the pipeline is addressed.

C. Change the imported pipeline to a different one: This does not address the disconnected pipeline; it only changes the Join stage’s configuration again, potentially causing further issues.

Deleting the disconnected pipeline ensures the dataset’s lineage is clean and free of errors, resolving the Stage Alert in the View Dataset Lineage report.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a Left Outer Join in a Join stage includes all rows from the primary pipeline (the left pipeline) and attempts to match them with rows from the imported pipeline (the right pipeline) based on the join condition. According to the official Workday Prism Analytics study path documents, if no matching rows exist in the imported pipeline for a given row in the primary pipeline, the row from the primary pipeline is still included in the output, but the fields from the imported pipeline will have NULL values. This behavior ensures that all data from the primary pipeline is retained, while the absence of a match in the imported pipeline is represented by NULLs for the corresponding fields.

The other options are incorrect:

A. A duplicate row will be generated: A Left Outer Join does not generate duplicate rows; duplicates would occur only if multiple matches exist in the imported pipeline, which is not the case here.

B. The row will be omitted: In a Left Outer Join, rows from the primary pipeline are never omitted, even if no match is found; this behavior is specific to an Inner Join.

D. Included fields from both pipelines will have NULL values: Only the fields from the imported pipeline will have NULL values; the fields from the primary pipeline retain their original values.

This behavior of Left Outer Join ensures that all records from the primary pipeline are preserved, with NULLs indicating the absence of matching data from the imported pipeline.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, the Explode stage transforms a multi-instance field (e.g., Language Skills) into multiple rows, creating a new single-instance field (e.g., Language Skills Exploded). The resulting field inherits the business object (BO) mapping from the original multi-instance field, but this mapping can be modified if needed. According to the official Workday Prism Analytics study path documents, to change the business object that the new Language Skills Exploded instance field is mapped to, you should add a Manage Fields stage after the Explode stage and modify the business object (option D).

The Manage Fields stage allows you to edit field properties, including the business object mapping, for the exploded field. After the Explode stage creates the new single-instance field, the Manage Fields stage can be used to reassign the business object by selecting a different Workday business object (e.g., changing from a generic object to a specific one like "Language"). This step ensures the field is mapped correctly for downstream reporting or integration with Workday reports.

The other options are incorrect:

A. Select from the list of suggested BO values in the Explode stage configuration: The Explode stage does not provide an option to modify business object mappings during its configuration; it focuses on exploding the multi-instance field.

B. Click on the Related Actions next to the business object in the insight panel: The insight panel provides metadata insights but does not allow direct modification of business object mappings for fields.

C. Add a Manage Fields before the Explode stage and modify the business object: Modifying the business object before the Explode stage affects the original multi-instance field, but the Explode stage will still create the new field with the inherited mapping, so this does not achieve the goal.

Adding a Manage Fields stage after the Explode stage is the correct approach to modify the business object mapping of the new exploded field.

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, blending two data sources typically involves joining them on a common field, such as Employee ID in this case. However, the Employee ID field is text in Pipeline 1 and numeric in Pipeline 2, which means the field types must be aligned before a join can be performed to avoid data mismatches or errors. According to the official Workday Prism Analytics study path documents, the correct approach is to first use a Manage Fields stage to change the field type of Employee ID in one of the pipelines to match the other (e.g., convert the numeric Employee ID in Pipeline 2 to text, as text can safely store numeric values without data loss), and then perform a Join stage to blend the data (option C). Converting from numeric to text is preferred because converting text to numeric risks data loss if the text field contains non-numeric characters.

The other options are not appropriate:

A. Add a Manage Fields to change the field type and then Union: A Union appends rows vertically and does not blend data based on a matching field like Employee ID; blending typically requires a Join.

B. Add a Filter first and then a Manage Fields to change the field type: Adding a Filter stage is unnecessary for preparing the field types for a join and does not address the blending requirement.

D. Add a Join first and then a Manage Fields to change the field type: Performing the Join first will fail or produce incorrect results because the field types (text and numeric) are incompatible for joining; the types must be aligned before the Join.

By using a Manage Fields stage to change the field type first and then performing a Join, the data from both pipelines can be blended accurately on the Employee ID field.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, when a data writer creates an intermediary calculated field (e.g., field A) solely to derive a final calculated field (e.g., field B) in a Derived Dataset (DDS), they may want to exclude the intermediary field from the published output to keep the dataset clean and focused. According to the official Workday Prism Analytics study path documents, the recommended approach is to add a Manage Fields stage to the DDS and hide field A. The Manage Fields stage allows users to control the visibility of fields in the dataset, enabling them to hide fields that are not needed in the final output while retaining their calculations for internal use within the dataset’s transformation logic. By hiding field A, field B can still leverage field A’s calculations, and only field B will be visible in the published dataset or data source.

The other options are not suitable:

A. Mark field A as intermediate calculation: There is no specific feature in Prism Analytics to “mark” a field as an intermediate calculation; this is not a supported action.

C. Add a Manage Fields stage to the DDS and hide field B: Hiding field B would defeat the purpose, as field B is the intended output to be published.

D. Delete field A from their DDS and just leave field B: Deleting field A would break the calculation of field B, as field B depends on field A, making this option infeasible.

Using the Manage Fields stage to hide field A ensures that the dataset remains functional while presenting only the necessary fields in the final output, aligning with best practices for data transformation and publishing.