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Workday Pro Prism Analytics Exam Sample Questions (Q27-Q32):

NEW QUESTION # 27
When joining datasets, what items must match?

  • A. The level of detail in each dataset.
  • B. The number of rows in each dataset.
  • C. The field names for the Match Row fields.
  • D. The field types for the Match Row fields.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, joining datasets requires that the fields used in the join condition (Match Row fields) are compatible to ensure accurate matching. According to the official Workday Prism Analytics study path documents, the field types for the Match Row fields must match (option A). For example, if the join condition is based on an Employee ID field, the field type (e.g., Text or Numeric) must be the same in both datasets. Mismatched field types (e.g., Text in one dataset and Numeric in another) can lead to join failures or incorrect results, as Prism cannot reliably compare values of different types. This often requires using a Manage Fields stage to align field types before the join.
The other options are incorrect:
* B. The number of rows in each dataset: The number of rows does not need to match; joins can handle datasets of different sizes, depending on the join type (e.g., Left Outer Join).
* C. The level of detail in each dataset: The level of detail (granularity) does not need to match; joins can combine datasets with different levels of detail as long as the Match Row fields are compatible.
* D. The field names for the Match Row fields: The field names do not need to be identical; the join condition maps fields between datasets, so different names can be used as long as the types and values are compatible.
Ensuring that the field types of the Match Row fields are the same is critical for a successful join operation in Prism Analytics.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic:
Requirements for Joining Datasets in Prism Analytics
Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Join Conditions for Datasets


NEW QUESTION # 28
You want to delete a Prism data source, but you are unable to complete the dataset unpublish. You have deleted the Report Writer reports. What else must you delete, if present?

  • A. Upstream tables
  • B. Published rows
  • C. Discovery board visualizations
  • D. Upstream datasets

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, unpublishing a dataset to delete a Prism data source requires removing all dependencies that reference the data source. According to the official Workday Prism Analytics study path documents, if you have already deleted Report Writer reports but are still unable to unpublish the dataset, you must also delete any discovery board visualizations that reference the Prism data source (option C). Discovery boards in Workday allow users to create visualizations based on Prism data sources, and these visualizations create a dependency on the data source. If such visualizations exist, the system prevents the dataset from being unpublished until they are removed, ensuring that no downstream dependencies are broken.
The other options are incorrect:
* A. Upstream tables: Upstream tables are the source of data for the dataset, but they are not dependencies of the published data source and do not need to be deleted to unpublish the dataset.
* B. Published rows: Published rows are part of the data source itself and are removed when the dataset is unpublished; they are not a separate dependency to delete.
* D. Upstream datasets: Upstream datasets may feed into the dataset being unpublished, but they are not dependencies of the published data source and do not prevent unpublishing.
Deleting discovery board visualizations ensures all dependencies are cleared, allowing the dataset to be unpublished and the Prism data source to be deleted.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic:
Unpublishing Prism Data Sources
Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Managing Dependencies for Data Source Deletion


NEW QUESTION # 29
A Prism data writer has two pipelines of data that need to be joined together:
* The primary pipeline includes point of sale data by sales agent.
* The secondary pipeline includes performance rating by sales agent.
The requirement is to keep all of the point of sale data from the primary pipeline and blend in performance rating data for the agents from the secondary pipeline where it exists. What Join type should be used to blend the data together?

  • A. Inner Join
  • B. Right Outer Join
  • C. Left Outer Join
  • D. Full Outer Join

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the requirement to keep all data from the primary pipeline (point of sale data by sales agent) and blend in matching data from the secondary pipeline (performance rating by sales agent) where it exists indicates the need for a specific type of join. According to the official Workday Prism Analytics study path documents, a Left Outer Join (option C) is the appropriate join type for this scenario. A Left Outer Join includes all rows from the primary pipeline and matches them with rows from the secondary pipeline based on the join condition (e.g., sales agent ID). If no match is found in the secondary pipeline, the fields from the secondary pipeline will have NULL values, but the primary pipeline's data is fully retained, meeting the requirement to keep all point of sale data while blending in performance ratings where available.
The other options do not meet the requirement:
* A. Inner Join: An Inner Join only includes rows where matches exist in both pipelines, which would exclude point of sale data for sales agents without performance ratings, violating the requirement to keep all primary pipeline data.
* B. Right Outer Join: A Right Outer Join includes all rows from the secondary pipeline and matching rows from the primary pipeline, which prioritizes the secondary pipeline and may exclude some point of sale data, not meeting the requirement.
* D. Full Outer Join: A Full Outer Join includes all rows from both pipelines, with NULLs for non- matching rows, but this is broader than the requirement, which only needs all data from the primary pipeline, not necessarily all data from the secondary pipeline.
A Left Outer Join ensures that all point of sale data is retained while blending in performance ratings where they exist, aligning with the stated requirement.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Join Types and Their Applications in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Blending Data Using Join Stages


NEW QUESTION # 30
Using three different source files, you want to load rows of data into an empty table through a Data Change task. What needs to be the same about the three source files?

  • A. Naming convention
  • B. Size
  • C. Source
  • D. Schema

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Data Change task is used to load or update data into a table, which can involve importing data from multiple source files. According to the official Workday Prism Analytics study path documents, when loading rows from multiple source files into an empty table, the source files must share the same schema. The schema defines the structure of the data, including the column names, data types, and their order, which ensures that the data from all source files can be consistently mapped and loaded into the target table without errors.
The schema is critical because the Data Change task relies on a predefined table structure to process the incoming data. If the schemas of the source files differ (e.g., different column names or data types), the task will fail due to inconsistencies in data mapping. The other options are not required to be the same:
* Source: The source files can originate from different systems or locations (e.g., Workday, external systems, or file uploads) as long as the schema aligns.
* Naming convention: The names of the source files do not need to follow a specific convention for the Data Change task to process them.
* Size: The size of the source files (e.g., number of rows or file size) can vary, as the task processes the data based on the schema, not the volume.
Thus, the requirement for the source files to have the same schema ensures seamless data loading into the table, maintaining data integrity and consistency during the transformation process.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Data Change Tasks and Schema Requirements Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Loading Data into Tables Using Data Change Tasks


NEW QUESTION # 31
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. Add the additional field to your raw data before you ingest into Prism.
  • B. Create a derived dataset and build a CASE calculated field to classify workers against their pay.
  • C. Build an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay.
  • 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 # 32
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