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You want your derived dataset to only show rows that meet the following criteria: Agent ID is not null AND Location is Dallas OR Location is Montreal. How can you achieve this?
A. By using Simple Filter conditions.
B. By adding a Manage Fields stage.
C. By using Advanced Filter conditions.
D. By creating a Custom Example.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, filtering a derived dataset to meet specific criteria involving multiple conditions with mixed logical operators (AND, OR) requires careful configuration. The criteria here are: Agent ID is not null AND (Location is Dallas OR Location is Montreal). According to the official Workday Prism Analytics study path documents, this can be achieved by using Advanced Filter conditions (option C).
A Simple Filter in Prism Analytics allows for basic conditions with a single operator ("If All" for AND, "If Any" for OR), but it cannot handle nested logic like AND combined with OR in a single filter. For example, a Simple Filter with "If All" would require all conditions to be true (Agent ID is not null AND Location is Dallas AND Location is Montreal), which is too restrictive. A Simple Filter with "If Any" would include rows where any condition is true (Agent ID is not null OR Location is Dallas OR Location is Montreal), which is too broad. The Advanced Filter, however, allows for complex expressions with nested logic, such as ISNOTNULL(Agent_ID) AND (Location = "Dallas" OR Location = "Montreal"), ensuring the correct rows are included.
The other options are incorrect:
* A. By adding a Manage Fields stage: The Manage Fields stage modifies field properties (e.g., type, visibility) but does not filter rows based on conditions.
* B. By using Simple Filter conditions: As explained, a Simple Filter cannot handle the combination of AND and OR logic required for this criteria.
* D. By creating a Custom Example: Custom Examples are used to provide sample data for testing, not to filter rows in a dataset.
Using Advanced Filter conditions allows for the precise application of the required logic to filter the dataset accurately.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Filtering Data in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using Advanced Filters for Complex Conditions
NEW QUESTION # 15
What task or report should you access to view a Prism data source?
A. Edit Dataset Transformations task
B. View Prism Data Source report
C. View Dataset Details report
D. Edit Data Source Security task
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Prism data source represents the published dataset that is available for reporting and analytics within the Workday ecosystem. According to the official Workday Prism Analytics study path documents, the "View Prism Data Source" report is the specific task or report designed to allow users to view the details of a Prism data source. This report provides comprehensive information about the data source, including its metadata, structure, and associated attributes, enabling users to understand the data available for reporting purposes.
The other options do not serve this purpose. The "Edit Dataset Transformations task" is used to modify the transformation logic applied to a dataset, not to view a data source. The "Edit Data Source Security task" focuses on managing security settings for a data source, such as access permissions, rather than viewing its contents. Similarly, the "View Dataset Details report" provides information about a dataset (including its metadata and sample rows) but does not specifically address the published Prism data source, which is a distinct entity created after a dataset is published.
The "View Prism Data Source" report is the correct choice as it directly aligns with the need to inspect the properties and structure of a Prism data source, ensuring users can verify its suitability for reporting or integration with Workday reports.
References:
Workday Prism Analytics Study Path Documents, Section: Datasets and Data Sources, Topic: Managing and Viewing Prism Data Sources Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Viewing and Validating Data Sources
NEW QUESTION # 16
You accidentally delete a Prism calculated field that is used in other Prism calculated fields or conditions.
What is a possible outcome?
A. The system will automatically adjust any dependencies accordingly.
B. Errors will result in any stage or calculated field that references the field.
C. Any calculated field referencing the deleted field defaults to zero.
D. The system will automatically reverse the deletion because the field is referenced elsewhere.
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, calculated fields are often interdependent, with one calculated field referencing another in its expression or being used in conditions within a dataset's transformation stages. According to the official Workday Prism Analytics study path documents, if a calculated field is deleted while other calculated fields or conditions depend on it, the system does not automatically handle the dependency. Instead, this deletion will cause errors in any stage or calculated field that references the deleted field. These errors occur because the dependent calculations or conditions can no longer resolve the reference to the deleted field, leading to failures in the dataset's transformation pipeline or when the dataset is processed or published.
The other options are incorrect:
A: The system will automatically reverse the deletion because the field is referenced elsewhere: Prism Analytics does not have an automatic reversal mechanism for deletions; users must manually restore the field if needed.
B: Any calculated field referencing the deleted field defaults to zero: The system does not default to zero; it will instead throw an error due to the unresolved reference.
D: The system will automatically adjust any dependencies accordingly: Prism does not automatically adjust dependencies; the user must manually update the dependent fields or conditions to resolve the issue.
The resulting errors highlight the importance of carefully managing dependencies when deleting calculated fields, ensuring that all references are updated or removed to avoid disruptions in the dataset's transformation logic.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Managing Calculated Fields and Dependencies Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Impact of Deleting Calculated Fields on Dataset Transformations
NEW QUESTION # 17
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 two Manage Fields transformation stages.
B. Up to five transformation stages.
C. As many transformation stages of any type as your scenario requires.
D. As many transformation stages of any type as long as they are in a particular order.
Answer: C
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 # 18
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. Right Outer Join
B. Full Outer Join
C. Left Outer Join
D. Inner 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 # 19
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