Workday-Prism-Analytics Workday Pro Prism Analytics Exam Questions and Answers

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.

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, calculated fields are defined within datasets, and their expressions dictate the logic used to compute their values. When issues like nil results arise, an administrator needs a centralized view to review and troubleshoot these expressions. According to the official Workday Prism Analytics study path documents, the View Dataset Lineage report is the tool that allows users to review the lineage of datasets, including the expressions of calculated fields, in bulk. This report provides a visual representation of the data lineage, showing the relationships between tables, datasets, and calculated fields, and allows users to drill into the details of each dataset to inspect the expressions of calculated fields across the lineage.

The other options are not as effective for this purpose:

A. The table or dataset where the calculated field was created: While you can review expressions in the specific dataset where a calculated field was created, this approach does not allow for a bulk review across multiple datasets in the lineage.

C. Any table in the lineage: Tables store raw data and do not contain calculated field expressions, which are defined in datasets.

D. Any dataset in the lineage: Reviewing datasets individually does not provide a bulk view of all calculated fields across the lineage, making it less efficient than the View Dataset Lineage report.

The View Dataset Lineage report is the most efficient way to review and troubleshoot calculated field expressions in bulk, enabling the administrator to identify and revise the problematic expressions causing nil results.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a Manage Fields stage is used to control the fields in a dataset by renaming, hiding, or changing field types, among other actions. According to the official Workday Prism Analytics study path documents, to ensure the correct set of fields is brought into a derived dataset (DDS), the Manage Fields stage should be applied at the beginning of the Primary Pipeline of the derived dataset (option C). Placing the Manage Fields stage early in the pipeline—right after the initial import stage (Stage 1)—allows you to define the field structure upfront, ensuring that subsequent transformationstages (e.g., Join, Filter, Calculate Field) operate on the desired set of fields. This approach helps maintain consistency and avoids unnecessary processing of fields that are not needed in later stages.

The other options are not optimal:

A. After the dataset is published: You cannot add transformation stages like Manage Fields after a dataset is published; transformations must be applied during the dataset’s creation or editing.

B. At the end of the Primary Pipeline of a published dataset: Similar to option A, you cannot modify a published dataset’s pipeline, and placing Manage Fields at the end would not prevent unnecessary fields from being processed in earlier stages.

D. At the beginning of the primary pipeline of the Base Dataset: A Base Dataset does not have a transformation pipeline; it is a direct import of a table, so Manage Fields stages can only be applied in a Derived Dataset.

Applying the Manage Fields stage at the beginning of the derived dataset’s Primary Pipeline ensures efficient data preparation and transformation.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, an Explode stage is used in a derived dataset to transform a multi-instance field into multiple rows, effectively creating single-instance fields for each instance. According to the official Workday Prism Analytics study path documents, the only field type you can select in the Explode stage is Multi-Instance (option B). A multi-instance field, often sourced from Workday data, contains multiple values for a single record (e.g., a list of certifications for a worker). The Explode stage “explodes” this field into separate rows, one for each instance, while converting the multi-instance fieldinto a single-instance field in the output. For example, if a worker has three certifications in a multi-instance field, the Explode stage would create three rows, each with a single certification value.

The other options are incorrect:

A. Numeric: Numeric fields are single-instance by nature and cannot be exploded, as they do not contain multiple values.

C. Single-Instance: Single-instance fields contain one value per record and cannot be used in an Explode stage, which requires a multi-instance field to operate.

D. Text: While a Text field can be single or multi-instance, the Explode stage specifically requires a Multi-Instance field, not just any Text field.

The requirement for a Multi-Instance field in the Explode stage ensures that the transformation can properly expand the data into multiple rows as intended.

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:

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, 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, 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, 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, calculated fields in a dataset are evaluated as part of the dataset’s processing logic, and their values are materialized when the dataset is published as a Prism data source. According to the official Workday Prism Analytics study path documents, the values of Prism calculated fields are calculated at the time of publishing (option D). When a dataset is published, Prism processes all transformation stages, including calculated fields, and the resulting values are stored in the publisheddata source. Workday reports that use this Prism data source then retrieve these pre-calculated values, ensuring consistent and efficient reporting without recalculating the fields at report run time.

The other options are incorrect:

A. At report run time: Calculated field values are not computed when the Workday report is run; they are pre-calculated and stored in the Prism data source during publishing.

B. At dataset creation time: Dataset creation involves defining the transformation logic, but the actual computation of calculated fields occurs during publishing, not at creation.

C. At the calculated field creation time: Creating a calculated field defines its expression, but the values are not computed until the dataset is processed during publishing.

The calculation of Prism calculated fields at the time of publishing ensures that Workday reports can efficiently access the results without additional computation overhead.

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.