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[General] Workday-Prism-Analytics Reliable Exam Prep - Workday-Prism-Analytics Testdump

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【General】 Workday-Prism-Analytics Reliable Exam Prep - Workday-Prism-Analytics Testdump

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Workday Pro Prism Analytics Exam Sample Questions (Q12-Q17):NEW QUESTION # 12
When using a window function to calculate averages in Prism, what field type must the function operate on?
  • A. Numeric
  • B. Text
  • C. Boolean
  • D. Date
Answer: A
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 # 13
An HR analyst is tasked to create custom reports for their company's performance reviews. The analyst uses both Workday and Prism for data analysis. What Workday-calculated field functions would the analyst be able to build off of their Prism object?
  • A. Extract Single Instance
  • B. Lookup Field with Prompts
  • C. Arithmetic Calculation
  • D. Lookup Related Value
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when integrating with Workday reports, a Prism object (i.e., a published Prism data source) can be used as the basis for custom reports, and certain Workday-calculated field functions can be applied to it. According to the official Workday Prism Analytics study path documents, the Arithmetic Calculation function (option B) is a supported Workday-calculated field function that can be built off a Prism object. This function allows the analyst to perform mathematical operations (e.g., addition, subtraction, multiplication) on numeric fields within the Prism data source, such as calculatinga performance review score by combining multiple metrics. Since Prism data sources often contain pre-processed data, arithmetic calculations can be applied to enhance the data for reporting purposes.
The other options are not supported for Prism objects:
* A. Extract Single Instance: This function is used to extract a single instance from a multi-instance field in Workday, but Prism objects typically contain single-instance fields after transformations (e.g., via an Explode stage), making this function inapplicable.
* C. Lookup Related Value: This function retrieves related values from other Workday business objects, but Prism objects do not support direct lookups to Workday objects in this manner; such relationships must be pre-built in the Prism dataset.
* D. Lookup Field with Prompts: This function involves interactive prompting, which is not supported for Prism objects in Workday reports, as Prism data sources are static snapshots of data.
The Arithmetic Calculation function provides the flexibility to perform numerical computations on Prism data, making it a suitable choice for enhancing performance review reports.
References:
Workday Prism Analytics Study Path Documents, Section: Integrating Prism with Workday Reports, Topic:
Using Calculated Fields with Prism Objects
Workday Prism Analytics Training Guide, Module: Integrating Prism with Workday Reports, Subtopic:
Supported Calculated Field Functions for Prism Data Sources

NEW QUESTION # 14
You accidentally delete a Prism calculated field that is used in other Prism calculated fields or conditions.
What is a possible outcome?
  • A. Errors will result in any stage or calculated field that references the field.
  • B. The system will automatically adjust any dependencies accordingly.
  • C. The system will automatically reverse the deletion because the field is referenced elsewhere.
  • D. Any calculated field referencing the deleted field defaults to zero.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, calculated fields are often interdependent, with one calculated field referencing another in its expression or being used in conditions within a dataset's transformation stages. According to the official Workday Prism Analytics study path documents, if a calculated field is deleted while other calculated fields or conditions depend on it, the system does not automatically handle the dependency. Instead, this deletion will cause errors in any stage or calculated field that references the deleted field. These errors occur because the dependent calculations or conditions can no longer resolve the reference to the deleted field, leading to failures in the dataset's transformation pipeline or when the dataset is processed or published.
The other options are incorrect:
A: The system will automatically reverse the deletion because the field is referenced elsewhere: Prism Analytics does not have an automatic reversal mechanism for deletions; users must manually restore the field if needed.
B: Any calculated field referencing the deleted field defaults to zero: The system does not default to zero; it will instead throw an error due to the unresolved reference.
D: The system will automatically adjust any dependencies accordingly: Prism does not automatically adjust dependencies; the user must manually update the dependent fields or conditions to resolve the issue.
The resulting errors highlight the importance of carefully managing dependencies when deleting calculated fields, ensuring that all references are updated or removed to avoid disruptions in the dataset's transformation logic.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Managing Calculated Fields and Dependencies Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Impact of Deleting Calculated Fields on Dataset Transformations

NEW QUESTION # 15
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 an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay.
  • B. Create a derived dataset and build a CASE calculated field to classify workers against their pay.
  • C. Add the additional field to your raw data before you ingest into Prism.
  • D. Build a CASE calculated field function on the TBL directly to ease later transformation.
Answer: B
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 # 16
For a Prism use case, you have two datasets: one contains daily sales data, and the other contains monthly budget allocations. Before performing a join between these datasets, what transformation stage should you apply to the sales data to ensure it matches the granularity of the budget data?
  • A. Group By
  • B. Manage Fields
  • C. Union
  • D. Filter
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, joining datasets with different levels of granularity requires aligning their granularity to ensure a meaningful match. The sales data is at a daily level (one row per day), while the budget data is at a monthly level (one row per month). According to the official Workday Prism Analytics study path documents, to match the granularity of the monthly budget data, you should apply a Group By stage to the sales data (option B). The Group By stage aggregates the daily sales data into monthly totals (e.g., summing sales amounts by month), reducing the granularity from daily to monthly. This allows the sales data to be joined with the monthly budget data on a common key, such as the month.
For example, a Group By stage could group the sales data by a derived month field (e.g., using a function like EXTRACT(YEAR_MONTH, sale_date)) and aggregate the sales amounts using a function like SUM (sales_amount). The resulting dataset would have one row per month, matching the budget data's granularity.
The other options are incorrect:
* A. Union: A Union stage appends rows from one dataset to another but does not change granularity; it cannot aggregate daily data into monthly data.
* C. Manage Fields: The Manage Fields stage modifies field properties (e.g., type, name) but does not aggregate data to change granularity.
* D. Filter: A Filter stage removes rows based on conditions but does not aggregate data to align granularity levels.
The Group By stage is the appropriate transformation to align the sales data's granularity with the monthly budget data for a successful join.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Aligning Granularity for Joins in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using Group By Stages for Data Aggregation

NEW QUESTION # 17
......
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