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Workday Pro Prism Analytics Exam Sample Questions (Q50-Q55):

NEW QUESTION # 50
A Prism data writer needs to create a new Prism calculated field on a derived dataset using the CASE function. When creating a calculated field, what symbol do you use to view a list of fields that you can select from in the dataset?

A. {
B. #
C. (
D. [

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when creating a calculated field in a derived dataset, users often need to reference existing fields in the dataset within their expressions, such as in a CASE function. According to the official Workday Prism Analytics study path documents, to view and select from a list of available fields in the dataset while building a calculated field expression, the user types the [ symbol (left square bracket). This symbol triggers a dropdown list of all fields in the dataset, allowing the user to select the desired field without manually typing its name, reducing the risk of errors. For example, typing [ and selecting a field like
"Employee_ID" will insert [Employee_ID] into the expression, which can then be used in the CASE function logic.
The other symbols do not serve this purpose:
* B. (: Parentheses are used for grouping expressions or defining function parameters, not for field selection.
* C. #: The hash symbol is not used in Prism Analytics for field selection; it may be associated with other functionalities in different contexts.
* D. {: Curly braces are not used for field selection in Prism Analytics; they may be used in other systems for different purposes, such as templating.
The use of the [ symbol ensures an efficient and accurate way to reference fields in a calculated field expression, streamlining the creation process in Prism Analytics.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Creating Calculated Fields in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using the Expression Editor for Calculated Fields

NEW QUESTION # 51
A Prism data writer has two pipelines of data that need to be joined together:
* The primary pipeline includes point of sale data by sales agent.
* The secondary pipeline includes performance rating by sales agent.
The requirement is to keep all of the point of sale data from the primary pipeline and blend in performance rating data for the agents from the secondary pipeline where it exists. What Join type should be used to blend the data together?

A. Full Outer Join
B. Inner Join
C. Right Outer Join
D. Left Outer Join

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the requirement to keep all data from the primary pipeline (point of sale data by sales agent) and blend in matching data from the secondary pipeline (performance rating by sales agent) where it exists indicates the need for a specific type of join. According to the official Workday Prism Analytics study path documents, a Left Outer Join (option C) is the appropriate join type for this scenario. A Left Outer Join includes all rows from the primary pipeline and matches them with rows from the secondary pipeline based on the join condition (e.g., sales agent ID). If no match is found in the secondary pipeline, the fields from the secondary pipeline will have NULL values, but the primary pipeline's data is fully retained, meeting the requirement to keep all point of sale data while blending in performance ratings where available.
The other options do not meet the requirement:
* A. Inner Join: An Inner Join only includes rows where matches exist in both pipelines, which would exclude point of sale data for sales agents without performance ratings, violating the requirement to keep all primary pipeline data.
* B. Right Outer Join: A Right Outer Join includes all rows from the secondary pipeline and matching rows from the primary pipeline, which prioritizes the secondary pipeline and may exclude some point of sale data, not meeting the requirement.
* D. Full Outer Join: A Full Outer Join includes all rows from both pipelines, with NULLs for non- matching rows, but this is broader than the requirement, which only needs all data from the primary pipeline, not necessarily all data from the secondary pipeline.
A Left Outer Join ensures that all point of sale data is retained while blending in performance ratings where they exist, aligning with the stated requirement.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Join Types and Their Applications in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Blending Data Using Join Stages

NEW QUESTION # 52
Using three different source files, you want to load rows of data into an empty table through a Data Change task. What needs to be the same about the three source files?

A. Schema
B. Size
C. Naming convention
D. Source

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Data Change task is used to load or update data into a table, which can involve importing data from multiple source files. According to the official Workday Prism Analytics study path documents, when loading rows from multiple source files into an empty table, the source files must share the same schema. The schema defines the structure of the data, including the column names, data types, and their order, which ensures that the data from all source files can be consistently mapped and loaded into the target table without errors.
The schema is critical because the Data Change task relies on a predefined table structure to process the incoming data. If the schemas of the source files differ (e.g., different column names or data types), the task will fail due to inconsistencies in data mapping. The other options are not required to be the same:
* Source: The source files can originate from different systems or locations (e.g., Workday, external systems, or file uploads) as long as the schema aligns.
* Naming convention: The names of the source files do not need to follow a specific convention for the Data Change task to process them.
* Size: The size of the source files (e.g., number of rows or file size) can vary, as the task processes the data based on the schema, not the volume.
Thus, the requirement for the source files to have the same schema ensures seamless data loading into the table, maintaining data integrity and consistency during the transformation process.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Data Change Tasks and Schema Requirements Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Loading Data into Tables Using Data Change Tasks

NEW QUESTION # 53
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. Group By
B. Union
C. Join
D. Explode

Answer: A

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

NEW QUESTION # 54
A Prism administrator wants to hide a field that contains employee salary information but still allow the Prism data writers to view average salaries for employees by cost center. What is the reason for hiding this field?

A. To hide unpopulated or sparse data fields.
B. To use computed values instead of base values.
C. To protect sensitive data.
D. To hide Prism-calculated fields used for interim processing.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, hiding a field is a common practice to control access to sensitive information while still allowing necessary analytics to be performed. According to the official Workday Prism Analytics study path documents, the primary reason for hiding a field like employee salary information is to protect sensitive data. Employee salary is considered personally identifiable information (PII) or sensitive data, and hiding the field ensures that individual salary details are not exposed to unauthorized users or in published data sources. However, by hiding the field, Prism data writers can still use it in calculations-such as computing the average salary by cost center-because hidden fields remain accessible for transformation and aggregation purposes within the dataset but are not visible in the final output or to end users of the published data source.
The other options do not align with the scenario:
* B. To hide Prism-calculated fields used for interim processing: The salary field is a base field, not a calculated field used for interim processing, so this reason does not apply.
* C. To hide unpopulated or sparse data fields: There is no indication that the salary field is unpopulated or sparse; the concern is about its sensitivity, not its data quality.
* D. To use computed values instead of base values: Hiding the field does not inherently involve replacing it with computed values; the goal is to restrict visibility while still allowing computations like averages.
Hiding the salary field protects sensitive data while enabling aggregated analytics, aligning with Prism's security and governance capabilities.
References:
Workday Prism Analytics Study Path Documents, Section: Security and Governance in Prism, Topic:
Managing Field Visibility for Data Protection
Workday Prism Analytics Training Guide, Module: Security and Governance in Prism, Subtopic: Handling Sensitive Data in Datasets

NEW QUESTION # 55
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Sam Bell Sam Bell

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