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

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Question #1 (Topic: Demo Questions)

What window function returns the number of rows within a window?

A.
MAX
B.
SUM
C.
COUNT
D.
AVG
Correct Answer: C
Explanation:
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.
[References:, , Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Window Functions and Their Applications, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using COUNT in Window Functions]
Question #2 (Topic: Demo Questions)

A Prism data administrator combined data from multiple sources down to a final derived dataset, including current worker data. There is a new requirement to append historical worker data to the dataset in a uniform layout. The historical worker data includes some, but not all, fields that align with the current worker data. Using current worker data as the primary pipeline, how can the historical worker data points be brought in?

A.
Add a Join stage with a Right Outer Join.
B.
Add a Union stage.
C.
Add a Join stage with a Left Outer Join.
D.
Add a Join stage with an Inner Join.
Correct Answer: B
Explanation:
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.
[References:, Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Using Union Stages to Append Data in Prism Analytics, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Combining Datasets with Union Operations, ]
Question #3 (Topic: Demo Questions)

You have to blend two sources of data. Your matching field is Employee ID, which is a text-type field in Pipeline 1, but is numeric in Pipeline 2. How do you prepare your data for blending?

A.
Add a Manage Fields to change the field type and then Union.
B.
Add a Filter first and then a Manage Fields to change the field type.
C.
Add first a Manage Fields to change the field type and then Join.
D.
Add a Join first and then a Manage Fields to change the field type.
Correct Answer: C
Explanation:
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.
[References:, Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Preparing Data for Joins in Prism Analytics, Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Field Type Transformations for Data Blending, ]
Question #4 (Topic: Demo Questions)

In a Prism project, you have a dataset containing customer purchase transactions, including the customer ID, purchase amount, and purchase date. You want to analyze the total purchase amount for each customer over the entire period. What transformation stage should you apply to calculate the total purchase amount for each customer?

A.
Join
B.
Union
C.
Group By
D.
Explode
Correct Answer: C
Explanation:
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, ]
Question #5 (Topic: Demo Questions)

You created a derived dataset that imports data from a table, which will become your Stage 1. What can you add to this dataset?

A.
As many transformation stages of any type as your scenario requires.
B.
As many transformation stages of any type as long as they are in a particular order.
C.
Up to five transformation stages.
D.
Up to two Manage Fields transformation stages.
Next Question
Correct Answer: A
Explanation:
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, ]
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