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

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, a Filter stage in a derived dataset is used to include only rows that meet specific criteria. The requirement here is to show expense reports that satisfy two conditions: (1) submitted in the current month, and (2) total amount higher than 2000 USD. According to the official Workday Prism Analytics study path documents, this can be achieved by using a simple filter with two conditions and the "If All" operator (option A).

The first condition would check the submission date, using a function like MONTH() to compare with the current month (e.g., MONTH(submission_date) = MONTH(CURRENT_DATE())). The second condition would compare the total amount (e.g., total_amount > 2000). The "If All" operator ensures that both conditions must be true for a row to be included, which aligns with the requirement that both criteria (current month AND amount > 2000 USD) must be met. A simple filter is sufficient because the logic involves straightforward comparisons without nested conditions.

The other options are incorrect:

B. Use a simple filter, two conditions, and "If Any" operator: The "If Any" operator would include rows where either condition is true (e.g., submitted in the current month OR amount > 2000 USD), which does not meet the requirement for both conditions to be true.

C. Use a simple filter, three conditions, and "If All" operator: Only two conditions are needed (submission month and amount), so three conditions are unnecessary.

D. Use a simple filter, three conditions, and "If Any" operator: This combines the issues of option B (wrong operator) and option C (too many conditions).

Using a simple filter with two conditions and the "If All" operator ensures the dataset includes only the expense reports that meet both criteria.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, before publishing a derived dataset as a Prism data source (PDS), it’s important to ensure that the dataset is properly configured for downstream use. According to the official Workday Prism Analytics study path documents, one key step to take prior to publishing is to edit the Dataset API Name to reflect in the name of the Prism data source (option D). The Dataset API Name determines the name of the published Prism data source, which is used in reporting, discovery boards, and integrations. Setting a meaningful and descriptive API name (e.g., "Expense_Reports_by_Location") ensures that the data source is easily identifiable and aligns with naming conventions, improving usability and manageability in the Workday ecosystem. This step is a best practice to avoid confusion and ensure clarity for report writers and analysts.

The other options are not required or relevant:

A. Add a Group By stage to the final derived dataset to add summary calculations: Adding a Group By stage is not mandatory unless the use case specifically requires summarizations, which is not indicated here.

B. Create a table without the Enable for Analysis checkbox selected: Creating a new table is unnecessary, as the dataset is already complete, and the "Enable for Analysis" checkbox is relevant for real-time updates, not a requirement for publishing a derived dataset.

C. Create a derived dataset with the PDS suffix: Creating a new dataset is not needed, as the final derived dataset is already prepared, and adding a "PDS" suffix is not a required step for publishing.

Editing the Dataset API Name ensures the Prism data source has a clear and meaningful name, facilitating its use in reporting and analytics.

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:

In Workday Prism Analytics, filtering a derived dataset to meet specific criteria involving multiple conditions with mixed logical operators (AND, OR) requires careful configuration. The criteria here are: Agent ID is not null AND (Location is Dallas OR Location is Montreal). According to the official Workday Prism Analytics study path documents, this can be achieved by using Advanced Filter conditions (option C).

A Simple Filter in Prism Analytics allows for basic conditions with a single operator ("If All" for AND, "If Any" for OR), but it cannot handle nested logic like AND combined with OR in a single filter. For example, a Simple Filter with "If All" would require all conditions to be true (Agent ID is not null AND Location is Dallas AND Location is Montreal), which is too restrictive. A Simple Filter with "If Any" would include rows where any condition is true (Agent ID is not null OR Location is Dallas OR Location is Montreal), which is too broad. The Advanced Filter, however, allows for complex expressions with nested logic, such as ISNOTNULL(Agent_ID) AND (Location = "Dallas" OR Location = "Montreal"), ensuring the correct rows are included.

The other options are incorrect:

A. By adding a Manage Fields stage: The Manage Fields stage modifies field properties (e.g., type, visibility) but does not filter rows based on conditions.

B. By using Simple Filter conditions: As explained, a Simple Filter cannot handle the combination of AND and OR logic required for this criteria.

D. By creating a Custom Example: Custom Examples are used to provide sample data for testing, not to filter rows in a dataset.

Using Advanced Filter conditions allows for the precise application of the required logic to filter the dataset accurately.

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 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, 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 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.