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Workday Pro Prism Analytics Exam Sample Questions (Q32-Q37):

NEW QUESTION # 32
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. Inner Join
B. Right Outer Join
C. Full 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 # 33
A custom report uses your recently published Prism data source, but you noticed a minor error in the published data. You need to delete the published rows to fix it. What happens to your custom report?

A. The report definition will be copied and a new version will appear after republishing.
B. The report definition remains intact and will work after republishing.
C. The report definition will need to be edited to reflect changes.
D. The report definition will need to be manually recreated.

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, deleting published rows from a Prism data source (PDS) is a step taken to correct errors in the published data, often followed by republishing the dataset with corrected data. According to the official Workday Prism Analytics study path documents, when you delete the published rows, the report definition remains intact and will work after republishing (option A). The custom report's definition, which is based on the Prism data source, is not affected by the deletion of published rows because the report definition references the data source's structure (e.g., fields and metadata), not the specific data rows. Once the dataset is republished with the corrected data, the report will automatically reflect the updated data without requiring any changes to the report definition, assuming the structure of the data source remains the same.
The other options are incorrect:
* B. The report definition will need to be manually recreated: The report definition is not deleted or invalidated by deleting published rows, so recreation is not necessary.
* C. The report definition will be copied and a new version will appear after republishing: Workday does not automatically copy or version report definitions when a data source is republished.
* D. The report definition will need to be edited to reflect changes: No edits are required unless the structure of the data source (e.g., field names or types) changes, which is not indicated in this scenario.
The report definition's integrity is maintained, and it will function as expected after republishing the corrected data.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic: Impact of Data Source Updates on Reports Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Managing Data Corrections in Prism Data Sources

NEW QUESTION # 34
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 hide Prism-calculated fields used for interim processing.
C. To use computed values instead of base values.
D. To protect sensitive data.

Answer: D

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 # 35
When using a window function to calculate averages in Prism, what field type must the function operate on?

A. Date
B. Boolean
C. Numeric
D. Text

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, window functions are used to perform calculations across a set of rows, such as calculating averages with a function like AVG. According to the official Workday Prism Analytics study path documents, the AVG window function, which computes the average, must operate on a fieldof type Numeric.
This is because averaging requires numerical values to perform arithmetic operations (e.g., summing the values and dividing by the count of rows). Non-numeric field types, such as Text or Date, cannot be averaged, and Boolean fields (true/false) are not suitable for this type of calculation. For example, a window function like AVG(salary) OVER (PARTITION BY department) would calculate the average salary per department, where "salary" must be a Numeric field.
The other options are incorrect:
* A. Text: Text fields cannot be used for arithmetic operations like averaging.
* B. Boolean: Boolean fields (true/false) are not suitable for calculating averages.
* D. Date: Date fields cannot be directly averaged; they require conversion to a numeric representation (e.
g., days since a reference date) first.
The requirement for a Numeric field type ensures that the AVG window function can perform the necessary mathematical computations accurately.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Window Functions and Field Type Requirements Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using AVG in Window Functions

NEW QUESTION # 36
The Prism use case is to classify workers based on their pay. You must create a field that evaluates worker pay and returns a value that represents various pay ranges. How would you add this field for inclusion on the Prism data source?

A. Build a CASE calculated field function on the TBL directly to ease later transformation.
B. Build an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay.
C. Create a derived dataset and build a CASE calculated field to classify workers against their pay.
D. Add the additional field to your raw data before you ingest into Prism.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, classifying workers into pay ranges based on their pay requires creating a new field that evaluates the pay values and assigns them to defined ranges (e.g., "Low," "Medium," "High").
According to the official Workday Prism Analytics study path documents, the recommended approach is to create a derived dataset (DDS) and build a CASE calculated field to classify workers against their pay (option B). The CASE function in a calculated field allows users to define conditional logic (e.g., CASE WHEN pay
< 50000 THEN "Low" WHEN pay < 100000 THEN "Medium" ELSE "High" END), which is ideal for creating pay range classifications. This calculated field is added within a deriveddataset, which can then be published as a Prism data source, making the new field available for reporting and analytics.
The other options are not optimal:
* A. Add the additional field to your raw data before you ingest into Prism: Modifying raw data outside Prism is unnecessary and less flexible, as Prism's transformation capabilities (like CASE) are designed for such tasks.
* C. Build a CASE calculated field function on the TBL directly to ease later transformation: Calculated fields cannot be created directly on a table (TBL) in Prism Analytics; they must be defined in a derived dataset.
* D. Build an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay: Prism Analytics does not use "Prism business objects" for calculated fields, and "Evaluate Expression" is not a standard function; this option is not applicable.
Using a CASE calculated field in a derived dataset provides a flexible and maintainable way to classify workers by pay ranges, ensuring the field is included in the final Prism data source.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Creating Calculated Fields with CASE Functions Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Classifying Data Using Calculated Fields in Derived Datasets

NEW QUESTION # 37
......

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