Workday-Prism-Analytics Workday Pro Prism Analytics Exam Free Practice Exam Questions (2025 Updated)

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.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, calculated fields are often interdependent, with one calculated field referencing another in its expression or being used in conditions within a dataset’s transformation stages. According to the official Workday Prism Analytics study path documents, if a calculated field is deleted while other calculated fields or conditions depend on it, the system does not automatically handle the dependency. Instead, this deletion will cause errors in any stage or calculated field that references the deleted field. These errors occur because the dependent calculations or conditions can no longer resolve the reference to the deleted field, leading to failures in the dataset’s transformation pipeline or when the dataset is processed or published.

The other options are incorrect:

A. The system will automatically reverse the deletion because the field is referenced elsewhere: Prism Analytics does not have an automatic reversal mechanism for deletions; users must manually restore the field if needed.

B. Any calculated field referencing the deleted field defaults to zero: The system does not default to zero; it will instead throw an error due to the unresolved reference.

D. The system will automatically adjust any dependencies accordingly: Prism does not automatically adjust dependencies; the user must manually update the dependent fields or conditions to resolve the issue.

The resulting errors highlight the importance of carefully managing dependencies when deleting calculated fields, ensuring that all references are updated or removed to avoid disruptions in the dataset’s transformation logic.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, an Explode stage is used to transform a multi-instance field into multiple rows, creating a single-instance field for each instance. According to the official Workday Prism Analyticsstudy path documents, when an Explode stage is applied to a derived dataset containing worker benefit elections (a multi-instance field, e.g., a list of elected benefits per worker), the following occurs: the number of rows exponentially increases, and the original multi-instance field gets dropped (option D).

For example, if a worker has three benefit elections in a multi-instance field, the Explode stage will create three rows—one for each election—while the original multi-instance field (e.g., "Benefit Elections") is replaced by a single-instance field containing one election per row. The number of rows increases based on the number of instances in the multi-instance field (e.g., a dataset with 100 workers, each with 3 elections on average, would grow from 100 rows to 300 rows). The term "exponentially" in the question reflects this potential for significant row growth, though the increase is technically linear per record based on the number of instances.

The other options are incorrect:

A. The number of columns stays the same and the original multi-instance field doesn't get dropped: The original multi-instance field is dropped and replaced by a single-instance field; the column count may change slightly due to this replacement.

B. The number of columns exponentially increases and the original multi-instance field gets dropped: The Explode stage does not increase the number of columns exponentially; it primarily affects rows, with minimal impact on columns.

C. The number of rows stays the same and the original multi-instance field does not get dropped: The number of rows increases due to the explosion of multi-instance data, and the original field is dropped.

The Explode stage’s behavior of increasing rows and dropping the original multi-instance field aligns with its purpose of normalizing multi-instance data into a row-based structure.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, the TO_DATE function in a calculated field is used to convert a string or compatible data type into a date. However, in this scenario, the field from Workday is numeric, and the TO_DATE function typically requires a string input (e.g., a numeric value like 20230101 needs to be converted to a string like "20230101" before applying TO_DATE). According to the official Workday Prism Analytics study path documents, to prepare the numeric field for use with the TO_DATE function, you must first use a Manage Fields stage to change the field’s Output Type to Text. The Manage Fields stage allows you to modify the field’s properties, and changing the Output Type from Numeric to Text converts the numeric values into a string format that the TO_DATE function can then process (e.g., TO_DATE([Field_Name], "YYYYMMDD")).

The other options are not relevant:

B. Output Name: Changing the Output Name renames the field but does not address the field type compatibility required for the TO_DATE function.

C. Input Type: The Manage Fields stage does not modify an "Input Type"; it adjusts the Output Type to transform the field as it moves through the pipeline.

D. Input Name: There is no "Input Name" property in the Manage Fields stage; this option is not applicable.

By changing the Output Type to Text in the Manage Fields stage, the numeric field is converted to a string, making it compatible with the TO_DATE function for creating a date field in the calculated field.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a derived dataset (DDS) allows users to transform data by adding various transformation stages after the initial import stage (Stage 1). According to the official Workday Prism Analytics study path documents, you can add as many transformation stages of any type as your scenario requires (option A). Prism Analytics supports a variety of transformation stages, such as Join, Union, Filter, Manage Fields, and Calculate Field, among others. There are no strict limits on the number of stages or their types, and they can be added in any order that makes sense for the data transformation logic, as long as the stages are configured correctly to produce the desired output. This flexibility allows users to build complex transformation pipelines tailored to their specific use case.

The other options are incorrect:

B. As many transformation stages of any type as long as they are in a particular order: While the order of stages matters for the transformation logic (e.g., a Filter before a Join), there is no predefined order requirement for all stages; the order depends on the scenario.

C. Up to five transformation stages: There is no limit of five transformation stages in Prism Analytics; you can add more as needed.

D. Up to two Manage Fields transformation stages: There is no restriction to only two Manage Fields stages; you can add as many as required.

The ability to add as many transformation stages as needed provides maximum flexibility in shaping the data within a derived dataset.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, the ability to create a dataset is governed by specific security domains within the Prism Analytics functional area. According to the official Workday Prism Analytics study path documents, the security domain that explicitly enables users to create datasets is the "Prism Datasets: Create" domain. This domain grants users the necessary permissions to initiate the creation of both base and derived datasets within the Prism Analytics Data Catalog.

The process of creating a dataset involves defining the metadata and processing logic to manipulate data, which can include Workday or external (non-Workday) data sources. The "Prism Datasets: Create" domain ensures that only authorized users, such as Prism data analysts or administrators, can perform this task, aligning with Workday’s configurable security framework. Other domains, such as "Prism Datasets: Publish," are responsible for publishing datasets to make them available as Prism Data Sources for reporting, while "Prism: Manage Data Source" pertains to managing the data sources themselves, not creating datasets. Similarly, "Prism: Tables Create" is related to creating tables, which is a distinct entity from datasets in Prism Analytics.

This distinction is critical, as datasets and tables serve different purposes in the data management workflow. Datasets include metadata and a subset of example rows, while tables contain metadata and all data rows. The "Prism Datasets: Create" domain is specifically designed to control access to dataset creation, ensuring secure and governed data preparation.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, joining datasets requires that the fields used in the join condition (Match Row fields) are compatible to ensure accurate matching. According to the official Workday Prism Analytics study path documents, the field types for the Match Row fields must match (option A). For example, if the join condition is based on an Employee ID field, the field type (e.g., Text or Numeric) must be the same in both datasets. Mismatched field types (e.g., Text in one dataset and Numeric in another) can lead to join failures or incorrect results, as Prism cannot reliably compare values of different types. This often requires using a Manage Fields stage to align field types before the join.

The other options are incorrect:

B. The number of rows in each dataset: The number of rows does not need to match; joins can handle datasets of different sizes, depending on the join type (e.g., Left Outer Join).

C. The level of detail in each dataset: The level of detail (granularity) does not need to match; joins can combine datasets with different levels of detail as long as the Match Row fields are compatible.

D. The field names for the Match Row fields: The field names do not need to be identical; the join condition maps fields between datasets, so different names can be used as long as the types and values are compatible.

Ensuring that the field types of the Match Row fields are the same is critical for a successful join operation in Prism Analytics.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, when using a custom report with prompts as a source connection for a table, the custom report must be configured to ensure compatibility with the Prism data ingestion process. According to the official Workday Prism Analytics study path documents, the key requirement is that the custom report prompts have default values assigned in the report definition. This is necessary because Prism Analytics does not support interactive prompting during data ingestion. Default values ensure that the report can run automatically without requiring user input, allowing the Data Change task to retrieve the data consistently and load it into the target table.

The other options are not correct in this context:

A. The report is built on an indexed data source: While indexed data sources can enhance performance for certain reports, they are not a requirement for using a custom report as a source for a Prism table.

B. The prompts are mapped at the data change task level: Prompts are not mapped in the Data Change task; instead, the task relies on the report’s default values to execute the data retrieval.

D. The prompts are marked as required: Marking prompts as required does not address the need for automatic execution; default values are still needed to avoid manual intervention.

By assigning default values to prompts in the custom report definition, the report can be seamlessly integrated as a source connection for Prism Analytics, ensuring reliable data loading into the table.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, calculating the total purchase amount for each customer requires aggregating data by customer ID. According to the official Workday Prism Analytics study path documents, the appropriate transformation stage for this task is a Group By stage (option C). The Group By stage allows you to group the dataset by a specific field (e.g., customer ID) and apply aggregation functions, such as SUM, to calculate the total purchase amount for each customer. For example, you would group by customer ID and use SUM(purchase_amount) to compute the total. This stage reduces the dataset to one row per customer, with the aggregated total purchase amount, enabling the desired analysis over the entire period.

The other options are incorrect:

A. Join: A Join stage combines data from two datasets based on a matching condition, but it does not aggregate data to calculate totals.

B. Union: A Union stage appends rows from one dataset to another, which does not help with calculating totals per customer.

D. Explode: An Explode stage transforms multi-instance fields into multiple rows, which is unrelated to aggregating purchase amounts.

The Group By stage is the correct choice to aggregate purchase amounts by customer, facilitating the analysis of totals over the entire period.

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:

To import a Workday custom report into the Prism Analytics Data Catalog, specific configurations are required to ensure the report is accessible and usable. According to the official Workday Prism Analytics study path documents, in addition to enabling the report as a web service and enabling it for Prism Analytics, the report must be shared with or owned by the user who is performing the import. This security requirement ensures that only authorized users can access and import the report into the Data Catalog, aligning with Workday’s configurable security model. The user must either be the owner of the report or have it shared with them through appropriate security permissions (e.g., via a security group or direct sharing).

The other options are incorrect:

A. It must be imported via sFTP: Custom reports are imported directly through Workday’s web service integration, not via sFTP, which is typically used for file-based data sources.

B. It must be built as a matrix report: There is no requirement for the report to be a matrix report; Prism Analytics supports various report types, including advanced and simple reports, as long as they are properly configured.

D. It must be tagged with a Prism Analytics report tag: Tagging is not a mandatory step for importing a report into the Data Catalog, though it may be used for organizational purposes.

Ensuring that the report is shared with or owned by the importing user is a critical step to maintain security and governance during the integration process.