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【Hardware】 New Workday-Prism-Analytics Exam Papers | Workday-Prism-Analytics Exam Fees

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Workday Pro Prism Analytics Exam Sample Questions (Q34-Q39):NEW QUESTION # 34
When using a window function to calculate averages in Prism, what field type must the function operate on?
  • A. Date
  • B. Numeric
  • C. Text
  • D. Boolean
Answer: B
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 # 35
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. Filter
  • B. Union
  • C. Group By
  • D. Manage Fields
Answer: C
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 # 36
You want to create a Prism calculated field to change the field type to date data using the TO_DATE function.
The field from Workday is numeric data and you will use the Manage Fields stage to prepare the data for use in the function. What will you need to change about the field in the Manage Fields stage?
  • A. Output Type
  • B. Input Name
  • C. Input Type
  • D. Output Name
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the TO_DATE function in a calculated field is used to convert a string or compatible data type into a date. However, in this scenario, the field from Workday is numeric, and the TO_DATE function typically requires a string input (e.g., a numeric value like 20230101 needs to be converted to a string like "20230101" before applying TO_DATE). According to the official Workday Prism Analytics study path documents, to prepare the numeric field for use with the TO_DATE function, you must first use a Manage Fields stage to change the field's Output Type to Text. The Manage Fields stage allows you to modify the field's properties, and changing the Output Type from Numeric to Text converts the numeric values into a string format that the TO_DATE function can then process (e.g., TO_DATE ([Field_Name], "YYYYMMDD")).
The other options are not relevant:
* B. Output Name: Changing the Output Name renames the field but does not address the field type compatibility required for the TO_DATE function.
* C. Input Type: The Manage Fields stage does not modify an "Input Type"; it adjusts the Output Type to transform the field as it moves through the pipeline.
* D. Input Name: There is no "Input Name" property in the Manage Fields stage; this option is not applicable.
By changing the Output Type to Text in the Manage Fields stage, the numeric field is converted to a string, making it compatible with the TO_DATE function for creating a date field in the calculated field.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Field Type Transformations for Calculated Fields Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using Manage Fields for Data Type Conversions

NEW QUESTION # 37
A Prism data administrator needs to add an Explode stage to a derived dataset to create a new single-instance type field. What is the only field type you can select in the Explode stage?
  • A. Numeric
  • B. Text
  • C. Single-Instance
  • D. Multi-Instance
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, an Explode stage is used in a derived dataset to transform a multi-instance field into multiple rows, effectively creating single-instance fields for each instance. According to the official Workday Prism Analytics study path documents, the only field type you can select in the Explode stage is Multi-Instance (option B). A multi-instance field, often sourced from Workday data, contains multiple values for a single record (e.g., a list of certifications for a worker). The Explode stage "explodes" this field into separate rows, one for each instance, while converting the multi-instance fieldinto a single-instance field in the output. For example, if a worker has three certifications in a multi-instance field, the Explode stage would create three rows, each with a single certification value.
The other options are incorrect:
* A. Numeric: Numeric fields are single-instance by nature and cannot be exploded, as they do not contain multiple values.
* C. Single-Instance: Single-instance fields contain one value per record and cannot be used in an Explode stage, which requires a multi-instance field to operate.
* D. Text: While a Text field can be single or multi-instance, the Explode stage specifically requires a Multi-Instance field, not just any Text field.
The requirement for a Multi-Instance field in the Explode stage ensures that the transformation can properly expand the data into multiple rows as intended.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Using the Explode Stage in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Handling Multi- Instance Fields with Explode Stages

NEW QUESTION # 38
You want to apply a Filter stage to your derived dataset to show only expense reports submitted in the current month and where the expense report total amount is higher than 2000 USD. What should you do?
  • A. Use a simple filter, three conditions, and "If Any" operator.
  • B. Use a simple filter, three conditions, and "If All" operator.
  • C. Use a simple filter, two conditions, and "If All" operator.
  • D. Use a simple filter, two conditions, and "If Any" operator.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Filter stage in a derived dataset is used to include only rows that meet specific criteria. The requirement here is to show expense reports that satisfy two conditions: (1) submitted in the current month, and (2) total amount higher than 2000 USD. According to the official Workday Prism Analytics study path documents, this can be achieved by using a simple filter with two conditions and the "If All" operator (option A).
The first condition would check the submission date, using a function like MONTH() to compare with the current month (e.g., MONTH(submission_date) = MONTH(CURRENT_DATE())). The second condition would compare the total amount (e.g., total_amount > 2000). The "If All" operator ensures that both conditions must be true for a row to be included, which aligns with the requirement that both criteria (current month AND amount > 2000 USD) must be met. A simple filter is sufficient because the logic involves straightforward comparisons without nested conditions.
The other options are incorrect:
* B. Use a simple filter, two conditions, and "If Any" operator: The "If Any" operator would include rows where either condition is true (e.g., submitted in the current month OR amount > 2000 USD), which does not meet the requirement for both conditions to be true.
* C. Use a simple filter, three conditions, and "If All" operator: Only two conditions are needed (submission month and amount), so three conditions are unnecessary.
* D. Use a simple filter, three conditions, and "If Any" operator: This combines the issues of option B (wrong operator) and option C (too many conditions).
Using a simple filter with two conditions and the "If All" operator ensures the dataset includes only the expense reports that meet both criteria.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Applying Filters in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Simple Filters with Multiple Conditions

NEW QUESTION # 39
......
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