Demo Workday Workday-Prism-Analytics Exam Questions

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.
[References:, Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Aggregating Data with Group By Stages, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using Group By for Summarization, ]

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.
[References:, Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Building Transformation Pipelines in Derived Datasets, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Adding and Configuring Transformation Stages, ]

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.
[References:, , Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Managing Fields in Derived Datasets, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Field Visibility in Datasets]