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Workday Pro Prism Analytics Exam Sample Questions (Q48-Q53):NEW QUESTION # 48
You want to convert each instance of a multi-instance field and convert it to a single-instance field. What transformation stage can you use to do this?
- A. Group By
- B. Manage Fields
- C. Explode
- D. Unpivot
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a multi-instance field contains multiple values for a single record (e.g., a list of skills for a worker). To convert each instance of a multi-instance field into a single-instance field, you need a transformation that expands the data into multiple rows, with each row containing one instance. According to the official Workday Prism Analytics study path documents, the Explode stage (option B) is the transformation stage designed for this purpose. The Explode stage takes a multi-instance field and creates a new row for each instance, transforming the multi-instance field into a single-instance field in the output. For example, if a worker has three skills in a multi-instance field, the Explode stage will create three rows, each with a single skill value in a single-instance field.
The other options are incorrect:
* A. Unpivot: Unpivot transforms columns into rows (e.g., converting wide data to long format), but it does not handle multi-instance fields, which are a specific Workday data type.
* C. Manage Fields: The Manage Fields stage modifies field properties (e.g., type, name) but cannot expand a multi-instance field into multiple rows.
* D. Group By: The Group By stage aggregates data (e.g., summing values by a key) but does not convert multi-instance fields into single-instance fields.
The Explode stage is the correct transformation to achieve the conversion of a multi-instance field into a single-instance field by expanding the data into multiple rows.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Handling Multi-Instance Fields with Explode Stages Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Converting Multi-Instance Fields to Single-Instance Fields
NEW QUESTION # 49
You want to import a Workday custom report into the data catalog. You have already enabled it as a web service and enabled it for Prism Analytics. What other configuration is required?
- A. It must be built as a matrix report.
- B. It must be tagged with a Prism Analytics report tag.
- C. It must be shared with or owned by the user importing the report.
- D. It must be imported via sFTP.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To import a Workday custom report into the Prism Analytics Data Catalog, specific configurations are required to ensure the report is accessible and usable. According to the official Workday Prism Analytics study path documents, in addition to enabling the report as a web service and enabling it for Prism Analytics, the report must be shared with or owned by the user who is performing the import. This security requirement ensures that only authorized users can access and import the report into the Data Catalog, aligning with Workday's configurable security model. The user must either be the owner of the report or have it shared with them through appropriate security permissions (e.g., via a security group or direct sharing).
The other options are incorrect:
* A. It must be imported via sFTP: Custom reports are imported directly through Workday's web service integration, not via sFTP, which is typically used for file-based data sources.
* B. It must be built as a matrix report: There is no requirement for the report to be a matrix report; Prism Analytics supports various report types, including advanced and simple reports, as long as they are properly configured.
* D. It must be tagged with a Prism Analytics report tag: Tagging is not a mandatory step for importing a report into the Data Catalog, though it may be used for organizational purposes.
Ensuring that the report is shared with or owned by the importing user is a critical step to maintain security and governance during the integration process.
References:
Workday Prism Analytics Study Path Documents, Section: Integrating Prism with Workday Reports, Topic:
Importing Custom Reports into the Data Catalog
Workday Prism Analytics Training Guide, Module: Datasets and Data Sources, Subtopic: Security Requirements for Report Integration
NEW QUESTION # 50
A Prism data writer needs to create a new Prism calculated field on a derived dataset using the CASE function. When creating a calculated field, what symbol do you use to view a list of fields that you can select from in the dataset?
Answer: C
Explanation:
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.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Creating Calculated Fields in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using the Expression Editor for Calculated Fields
NEW QUESTION # 51
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 an Inner Join.
- B. Add a Union stage.
- C. Add a Join stage with a Right Outer Join.
- D. Add a Join stage with a Left 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 # 52
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. Full Outer Join
- C. Right Outer Join
- D. Left Outer Join
Answer: D
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 # 53
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