Workday-Prism-Analytics Exam Dumps - Workday Pro Prism Analytics Exam

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, unpublishing a dataset to delete a Prism data source requires removing all dependencies that reference the data source. According to the official Workday Prism Analytics study path documents, if you have already deleted Report Writer reports but are still unable to unpublish the dataset, you must also delete any discovery board visualizations that reference the Prism data source (option C). Discovery boards in Workday allow users to create visualizations based on Prism data sources, and these visualizations create a dependency on the data source. If such visualizations exist, the system prevents the dataset from being unpublished until they are removed, ensuring that no downstream dependencies are broken.

The other options are incorrect:

A. Upstream tables: Upstream tables are the source of data for the dataset, but they are not dependencies of the published data source and do not need to be deleted to unpublish the dataset.

B. Published rows: Published rows are part of the data source itself and are removed when the dataset is unpublished; they are not a separate dependency to delete.

D. Upstream datasets: Upstream datasets may feed into the dataset being unpublished, but they are not dependencies of the published data source and do not prevent unpublishing.

Deleting discovery board visualizations ensures all dependencies are cleared, allowing the dataset to be unpublished and the Prism data source to be deleted.

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, 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:

Window functions in Workday Prism Analytics are a powerful feature used in dataset transformations to perform advanced calculations. According to the official Workday Prism Analytics study path documents, the primary purpose of window functions is to perform calculations across a set of rows related to the current row while partitioning the data. These functions allow users to compute values such as running totals, rankings, or aggregations (e.g., SUM, COUNT, RANK) within a defined “window” of rows, which can be partitioned by specific columns and ordered as needed. Window functions operate withoutcollapsing the dataset (unlike group-by aggregations), preserving the original row structure while adding calculated results.

The other options do not describe the purpose of window functions:

A. To provide row-level access control: Row-level access control is managed through security domains and policies, not window functions.

B. To manipulate strings and dates within a query: String and date manipulations are handled by other functions (e.g., CONCAT, DATEADD), not window functions.

C. To filter rows based on specified conditions: Filtering is achieved using WHERE clauses or filter stages, not window functions.

Window functions are essential for complex analytical calculations, such as ranking employees within a department or calculating cumulative totals, making them a key tool in Prism’s data transformation capabilities.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, managing schedules for importing data into tables or publishing datasets as Prism data sources is a key administrative task. According to the official Workday Prism Analytics study path documents, the Scheduled Future Processes report (option A) is the tool used to edit and maintain Prism import and publish schedules. This report provides a centralized view of all scheduled processes in Workday, including Prism-related tasks such as Data Change tasks (for imports) and dataset publish schedules. Users can access this report to view, edit, or cancel scheduled processes, ensuring that data imports and publishes occur at the desired frequency and time.

The other options are incorrect:

B. Prism Management Console: The Prism Management Console provides an overview of Prism activities and resources but does not allow for editing or maintaining schedules.

C. Prism Activities Monitor: This report monitors the status of Prism activities (e.g., running or completed tasks) but does not manage schedules.

D. Prism Usage: The Prism Usage report tracks usage metrics for Prism Analytics but does not handle scheduling tasks.

The Scheduled Future Processes report is the correct tool for managing Prism import and publish schedules, ensuring efficient data updates.

In Workday Prism Analytics, joining two tables requires that the fields used in the join condition have compatible data types to avoid data mismatches or loss. The Employee Data TBL has an Employee ID field as text, while the Learner Data TBL has an Employee ID field as numeric. According to the official Workday Prism Analytics study path documents, to join these tables without potential data loss, the best approach is to convert the numeric Employee ID in the Learner Data TBL to text, as text fields can safely store numeric values as strings, but converting text to numeric risks data loss if the text field contains non-numeric characters (e.g., leading zeros or special characters).

The correct method is to import the Learner Data TBL into a Derived Dataset (DDS) and use a Manage Fields stage to change the field type of Employee ID from numeric to text (option D). This ensures that the Employee ID field in both tables is text, enabling a safe and accurate join without losing data. The Manage Fields stage in a DDS allows for field type transformations, which is the recommended approach for preparing data for joins in Prism Analytics.

The other options are less suitable:

A. Import the Employee Data TBL into a DDS and change the field type of Employee ID from text to numeric using a Manage Fields stage: Converting text to numeric risks data loss if the text field contains non-numeric values, which could lead to errors or missing records during the join.

B. Change the field type of Employee ID directly on the Employee Data TBL from text to numeric: Direct field type changes on tables are not supported in Prism Analytics, and even if possible, this approach risks data loss for the same reason as option A.

C. Change the field type of Employee ID directly on the Learner Data TBL from Numeric to Text: Direct field type changes on tables are not supported; field type transformations must be done in a DDS using a Manage Fields stage.

By converting the numeric Employee ID to text in a DDS, the join can be performed safely, preserving all data from both tables.