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

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a matrix report in Workday is designed to display summarized data in a grid format, with built-in capabilities to calculate aggregations like minimum, maximum, total, average, and median. According to the official Workday Prism Analytics study path documents, to produce a Prism data source for a matrix report that needs to display the minimum, maximum, total, average, and median purchase order amount by location and month, you should publish your detail data and build the summarizations in the matrix report (option A).

Publishing the detail data (i.e., the raw purchase order data with fields like location, month, and amount) as a Prism data source allows the matrix report to access the granular data. The matrix report can then apply the required aggregations (MIN, MAX, SUM, AVG, MEDIAN) directly, grouping by location and month as specified in the report configuration. This approach leverages Workday’s reporting capabilities, reducing the need for additional transformations in Prism and ensuring flexibility for future reporting needs.

The other options are less efficient:

B. Add a Group By stage to your derived dataset: A Group By stage in the derived dataset can compute some aggregations (e.g., SUM, AVG), but Prism does not natively support calculating the median in a Group By stage, and it would require multiple stages or calculated fields to compute all metrics, making it less practical than using the matrix report.

C. Publish your detail data and build the summarizations in the advanced report: While an advanced report can perform some summarizations, it is not as well-suited as a matrix report for displaying multiple aggregations (like median) in a grid format by location and month.

D. Add two Group By stages to your derived dataset: Using two Group By stages is unnecessary and still does not address the limitation of calculating the median in Prism, making this approach overly complex.

Publishing the detail data and letting the matrix report handle the summarizations is the most efficient and effective way to meet the requirements.

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.

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.