FEATURES OF WORKDAY-PRISM-ANALYTICS PRACTICE QUESTIONS FORMATS

Features Of Workday-Prism-Analytics Practice Questions Formats

Features Of Workday-Prism-Analytics Practice Questions Formats

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Workday Pro Prism Analytics Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
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. Text
  • B. Numeric
  • C. Multi-Instance
  • D. Single-Instance

Answer: C

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 # 44
What is the primary purpose of window functions in Prism?

  • A. To filter rows based on specified conditions.
  • B. To provide row-level access control.
  • C. To perform calculations across a set of rows related to the current row while partitioning the data.
  • D. To manipulate strings and dates within a query.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Window functions in Workday Prism Analytics are a powerful feature used in dataset transformations to perform advanced calculations. According to the official Workday Prism Analytics study path documents, the primary purpose of window functions is to perform calculations across a set of rows related to the current row while partitioning the data. These functions allow users to compute values such as running totals, rankings, or aggregations (e.g., SUM, COUNT, RANK) within a defined "window" of rows, which can be partitioned by specific columns and ordered as needed. Window functions operate withoutcollapsing the dataset (unlike group-by aggregations), preserving the original row structure while adding calculated results.
The other options do not describe the purpose of window functions:
A: To provide row-level access control: Row-level access control is managed through security domains and policies, not window functions.
B: To manipulate strings and dates within a query: String and date manipulations are handled by other functions (e.g., CONCAT, DATEADD), not window functions.
C: To filter rows based on specified conditions: Filtering is achieved using WHERE clauses or filter stages, not window functions.
Window functions are essential for complex analytical calculations, such as ranking employees within a department or calculating cumulative totals, making them a key tool in Prism's data transformation capabilities.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Using Window Functions in Dataset Transformations Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Advanced Calculations with Window Functions


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 Union stage.
  • B. Add a Join stage with a Left Outer Join.
  • C. Add a Join stage with a Right Outer Join.
  • D. Add a Join stage with an Inner Join.

Answer: A

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 created a derived dataset that imports data from a table, which will become your Stage 1. What can you add to this dataset?

  • A. Up to five transformation stages.
  • B. As many transformation stages of any type as long as they are in a particular order.
  • C. Up to two Manage Fields transformation stages.
  • D. As many transformation stages of any type as your scenario requires.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a derived dataset (DDS) allows users to transform data by adding various transformation stages after the initial import stage (Stage 1). According to the official Workday Prism Analytics study path documents, you can add as many transformation stages of any type as your scenario requires (option A). Prism Analytics supports a variety of transformation stages, such as Join, Union, Filter, Manage Fields, and Calculate Field, among others. There are no strict limits on the number of stages or their types, and they can be added in any order that makes sense for the data transformation logic, as long as the stages are configured correctly to produce the desired output. This flexibility allows users to build complex transformation pipelines tailored to their specific use case.
The other options are incorrect:
* B. As many transformation stages of any type as long as they are in a particular order: While the order of stages matters for the transformation logic (e.g., a Filter before a Join), there is no predefined order requirement for all stages; the order depends on the scenario.
* C. Up to five transformation stages: There is no limit of five transformation stages in Prism Analytics; you can add more as needed.
* D. Up to two Manage Fields transformation stages: There is no restriction to only two Manage Fields stages; you can add as many as required.
The ability to add as many transformation stages as needed provides maximum flexibility in shaping the data within a derived dataset.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Building Transformation Pipelines in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Adding and Configuring Transformation Stages


NEW QUESTION # 47
You want to configure access to a published Prism data source to use it in reporting and discovery boards.
What action must you take?

  • A. Share the imported Workday report to provide users with access to the published Prism data source.
  • B. Schedule the recurring publish process.
  • C. Edit the data source security and select a domain.
  • D. Share the dataset with appropriate users.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, configuring access to a published Prism data source for use in reporting and discovery boards requires managing its security settings. According to the official Workday Prism Analytics study path documents, the necessary action is to edit the data source security and select a domain (option A).
After a dataset is published as a Prism data source, access is controlled through security domains. By editing the data source security and assigning it to an appropriate security domain (e.g., a domain that grants access to specific user groups like report writers or analysts), you ensure that authorized users can access the data source for reporting and discovery boards. This aligns with Workday's configurable security framework, ensuring that only users with the appropriate permissions can view or use the data source.
The other options are incorrect:
* B. Share the dataset with appropriate users: Sharing the dataset itself does not grant access to the published Prism data source; access to the data source is controlled through its security settings, not the dataset's sharing settings.
* C. Share the imported Workday report to provide users with access to the published Prism data source:
Sharing an imported Workday report does not affect access to the Prism data source; the data source's security must be configured directly.
* D. Schedule the recurring publish process: Scheduling a recurring publish process ensures the data source is updated regularly, but it does not configure access for reporting or discovery boards.
Editing the data source security and selecting a domain is the critical step to enable access for reporting and discovery boards.
References:
Workday Prism Analytics Study Path Documents, Section: Security and Governance in Prism, Topic:
Configuring Access to Prism Data Sources
Workday Prism Analytics Training Guide, Module: Security and Governance in Prism, Subtopic: Managing Data Source Security for Reporting


NEW QUESTION # 48
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