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

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 published data 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:

In Workday Prism Analytics, a Prism data source represents the published dataset that is available for reporting and analytics within the Workday ecosystem. According to the official Workday Prism Analytics study path documents, the "View Prism Data Source" report is the specific task or report designed to allow users to view the details of a Prism data source. This report provides comprehensive information about the data source, including its metadata, structure, and associated attributes, enabling users to understand the data available for reporting purposes.

The other options do not serve this purpose. The "Edit Dataset Transformations task" is used to modify the transformation logic applied to a dataset, not to view a data source. The "Edit Data Source Security task" focuses on managing security settings for a data source, such as access permissions, rather than viewing its contents. Similarly, the "View Dataset Details report" provides information about a dataset (including its metadata and sample rows) but does not specifically address the published Prism data source, which is a distinct entity created after a dataset is published.

The "View Prism Data Source" report is the correct choice as it directly aligns with the need to inspect the properties and structure of a Prism data source, ensuring users can verify its suitability for reporting or integration with Workday reports.

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, 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.

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.

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, 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:

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, 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, when the goal is to append data from one dataset to another in a uniform layout, such as combining current worker data with historical worker data, a Union stage is the appropriate transformation. According to the official Workday Prism Analytics study path documents, a Union stage is used to append rows from one pipeline to another, stacking the data vertically while aligning fields based on their names and types. In this scenario, the current worker data (primary pipeline) and historical worker data (secondary pipeline) share some fields, and a Union stage will combine the rows from both datasets into a single dataset. Fields that exist in one pipeline but not the other will have NULL values for the rows where they are not present, ensuring a uniform layout without losing data.

The other options are not suitable for this requirement:

A. Add a Join stage with a Right Outer Join: A Right Outer Join would include all rows from the historical worker data and only matching rows from the current worker data, which does not align with the goal of appending all data in a uniform layout.

C. Add a Join stage with a Left Outer Join: A Left Outer Join would include all rows from the current worker data and matching rows from the historical worker data, but this is not an append operation; it’s a matching operation based on a join condition, which isn’t specified here.

D. Add a Join stage with an Inner Join: An Inner Join would only include rows where matches exist between the two datasets, potentially excluding non-matching historical or current worker data, which does not meet the requirement to append all data.

The Union stage is the correct approach to append historical worker data to the current worker data, ensuring all rows are included in a uniform layout, with NULLs filling in for missing fields.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a published Prism data source (PDS) contains a snapshot of data from a derived dataset at the time of publishing. According to the official Workday Prism Analytics study path documents, for reports using a Prism data source, the data is updated at reimport into tables and republish of the datasource (option B). A derived dataset typically sources data from underlying tables (via import stages), and any updates to the source data require two steps: (1) reimporting the updated data into the tables (e.g., via a Data Change task), and (2) republishing the derived dataset to refresh the Prism data source with the new data. Reports using the PDS will reflect the updated data only after both steps are completed, as the data source is a static snapshot until republished.

The other options are incorrect:

A. At republish of the datasource only: Republishing alone does not update the data if the underlying tables have not been reimported with new data; both steps are necessary.

C. At reimport into tables only: Reimporting into tables updates the source data, but the PDS remains unchanged until the dataset is republished.

D. At report runtime: Reports do not dynamically update the PDS at runtime; they use the data as it exists in the PDS at the time of the last publish.

The combination of reimporting into tables and republishing the data source ensures that reports reflect the most current data.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, window functions are used to perform calculations over a defined set of rows (a “window”). According to the official Workday Prism Analytics study path documents, the COUNT window function is used to return the number of rows within a specified window. When applied in a dataset transformation, the COUNT function counts the rows that fall within the window, which can be defined by partitioning (e.g., by a specific column) and ordering criteria. For example, COUNT(*) OVER (PARTITION BY department) would return the number of rows for each department in the dataset.

The other options serve different purposes:

A. MAX: Returns the maximum value within the window, not the number of rows.

B. SUM: Calculates the sum of a numeric field across the window, not the row count.

D. AVG: Computes the average of a numeric field within the window, not the row count.

The COUNT function is specifically designed to provide the row count, making it the correct choice for this purpose in Prism Analytics transformations.