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[General] Valid Workday-Prism-Analytics Test Notes | Workday-Prism-Analytics Valid Study M

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【General】 Valid Workday-Prism-Analytics Test Notes | Workday-Prism-Analytics Valid Study M

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Workday Pro Prism Analytics Exam Sample Questions (Q45-Q50):NEW QUESTION # 45
A Prism data administrator combined data from multiple sources down to a final derived dataset, including current worker data. There is a new requirement to append historical worker data to the dataset in a uniform layout. The historical worker data includes some, but not all, fields that align withthe current worker data.
Using current worker data as the primary pipeline, how can the historical worker data points be brought in?
  • A. Add a Join stage with a Left Outer Join.
  • B. Add a Union stage.
  • C. Add a Join stage with an Inner Join.
  • D. Add a Join stage with a Right Outer Join.
Answer: B
Explanation:
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.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Using Union Stages to Append Data in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Combining Datasets with Union Operations

NEW QUESTION # 46
You explode the Language Skills multi-instance field on your derived dataset and you want to change the business object that the new Language Skills Exploded instance field is mapped to. What steps should you take?
  • A. Select from the list of suggested BO values in the Explode stage configuration.
  • B. Click on the Related Actions next to the business object in the insight panel.
  • C. Add a Manage Fields after the Explode stage and modify the business object.
  • D. Add a Manage Fields before the Explode stage and modify the business object.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the Explode stage transforms a multi-instance field (e.g., Language Skills) into multiple rows, creating a new single-instance field (e.g., Language Skills Exploded). The resulting field inherits the business object (BO) mapping from the original multi-instance field, but this mapping can be modified if needed. According to the official Workday Prism Analytics study path documents, to change the business object that the new Language Skills Exploded instance field is mapped to, you should add a Manage Fields stage after the Explode stage and modify the business object (option D).
The Manage Fields stage allows you to edit field properties, including the business object mapping, for the exploded field. After the Explode stage creates the new single-instance field, the Manage Fields stage can be used to reassign the business object by selecting a different Workday business object (e.g., changing from a generic object to a specific one like "Language"). This step ensures the field is mapped correctly for downstream reporting or integration with Workday reports.
The other options are incorrect:
* A. Select from the list of suggested BO values in the Explode stage configuration: The Explode stage does not provide an option to modify business object mappings during its configuration; it focuses on exploding the multi-instance field.
* B. Click on the Related Actions next to the business object in the insight panel: The insight panel provides metadata insights but does not allow direct modification of business object mappings for fields.
* C. Add a Manage Fields before the Explode stage and modify the business object: Modifying the business object before the Explode stage affects the original multi-instance field, but the Explode stage will still create the new field with the inherited mapping, so this does not achieve the goal.
Adding a Manage Fields stage after the Explode stage is the correct approach to modify the business object mapping of the new exploded field.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Managing Field Properties After Explode Stages Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Modifying Business Object Mappings in Derived Datasets

NEW QUESTION # 47
You have two tables. One with employee data from Workday and another with learner data from an external system. Both tables have an Employee ID field.
In the Employee Data TBL, Employee ID is text.

In the Learner Data TBL, Employee ID is numeric.

How can you prepare to join these tables, without the potential loss of data?
  • A. Change the field type of Employee ID directly on the Employee Data TBL from text to numeric.
  • B. Change the field type of Employee ID directly on the Learner Data TBL from Numeric to Text.
  • C. Import the Employee Data TBL into a DDS and change the field type of Employee ID from text to numeric using a Manage Fields stage.
  • D. Import the Learner Data TBL into a DDS and change the field type of Employee ID from numeric to text using a Manage Fields stage.
Answer: D
Explanation:
In Workday Prism Analytics, joining two tables requires that the fields used in the join condition have compatible data types to avoid data mismatches or loss. The Employee Data TBL has an Employee ID field as text, while the Learner Data TBL has an Employee ID field as numeric. According to the official Workday Prism Analytics study path documents, to join these tables without potential data loss, the best approach is to convert the numeric Employee ID in the Learner Data TBL to text, as text fields can safely store numeric values as strings, but converting text to numeric risks data loss if the text field contains non-numeric characters (e.g., leading zeros or special characters).
The correct method is to import the Learner Data TBL into a Derived Dataset (DDS) and use a Manage Fields stage to change the field type of Employee ID from numeric to text (option D). This ensures that the Employee ID field in both tables is text, enabling a safe and accurate join without losing data. The Manage Fields stage in a DDS allows for field type transformations, which is the recommended approach for preparing data for joins in Prism Analytics.
The other options are less suitable:
* A. Import the Employee Data TBL into a DDS and change the field type of Employee ID from text to numeric using a Manage Fields stage: Converting text to numeric risks data loss if the text field contains non-numeric values, which could lead to errors or missing records during the join.
* B. Change the field type of Employee ID directly on the Employee Data TBL from text to numeric:
Direct field type changes on tables are not supported in Prism Analytics, and even if possible, this approach risks data loss for the same reason as option A.
* C. Change the field type of Employee ID directly on the Learner Data TBL from Numeric to Text:
Direct field type changes on tables are not supported; field type transformations must be done in a DDS using a Manage Fields stage.
By converting the numeric Employee ID to text in a DDS, the join can be performed safely, preserving all data from both tables.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Preparing Data for Joins in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Field Type Transformations Using Manage Fields Stage

NEW QUESTION # 48
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 # 49
You had to change the imported pipeline in a Join stage and your View Dataset Lineage report shows a Stage Alert regarding the disconnected pipeline. How can you fix this and make the alert disappear?
  • A. Publish the derived dataset.
  • B. Delete the disconnected pipeline.
  • C. Change the imported pipeline to a different one.
  • D. Add a Manage Fields stage and re-attach the pipeline.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Stage Alert in the View Dataset Lineage report indicates an issue with the dataset's transformation pipeline, such as a disconnected pipeline resulting from changing the imported pipeline in a Join stage. According to the official Workday Prism Analytics study path documents, a disconnected pipeline occurs when a pipeline (e.g., a table or dataset) is no longer referenced by any transformation stage, often after modifying the Join stage to use a different imported pipeline. To resolve this alert, the recommended action is to delete the disconnected pipeline (option D). By removing the disconnected pipeline from the dataset, the lineage is updated to reflect only the active pipelines, and the Stage Alert will disappear, indicating that the dataset's configuration is now valid.
The other options are not appropriate:
* A. Add a Manage Fields stage and re-attach the pipeline: A Manage Fields stage modifies field properties and cannot re-attach a disconnected pipeline to the Join stage.
* B. Publish the derived dataset: Publishing the dataset does not resolve the issue of a disconnected pipeline; the alert will persist until the pipeline is addressed.
* C. Change the imported pipeline to a different one: This does not address the disconnected pipeline; it only changes the Join stage's configuration again, potentially causing further issues.
Deleting the disconnected pipeline ensures the dataset's lineage is clean and free of errors, resolving the Stage Alert in the View Dataset Lineage report.
References:
Workday Prism Analytics Study Path Documents, Section: Datasets and Data Sources, Topic:
Troubleshooting Stage Alerts in Dataset Lineage
Workday Prism Analytics Training Guide, Module: Datasets and Data Sources, Subtopic: Managing Pipeline Connections in Derived Datasets

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