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Workday Pro Prism Analytics Exam 認定 Workday-Prism-Analytics 試験問題 (Q26-Q31):
質問 # 26
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. Manage Fields
- B. Union
- C. Filter
- D. Group By
正解:D
解説:
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
質問 # 27
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 shared with or owned by the user importing the report.
- C. It must be tagged with a Prism Analytics report tag.
- D. It must be imported via sFTP.
正解:B
解説:
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
質問 # 28
You just imported your table on worker compensation into a derived dataset but before adding any transformation you want to make sure you have no NULL values for the Worker ID field. How can you get this insight?
- A. Add a Manage Fields stage.
- B. Click on the field name and check the stage statistics.
- C. Join on the Worker ID field.
- D. Create a Prism calculated field.
正解:B
解説:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, after importing a table into a derived dataset (DDS), you can inspect the data for quality issues, such as NULL values, before proceeding with transformations. According to the official Workday Prism Analytics study path documents, to check for NULL values in a specific field like Worker ID, the most direct method is to click on the field name and check the stage statistics. When viewing a dataset in the Prism Analytics interface, clicking on a field name (e.g., Worker ID) in the dataset preview displays stage statistics, which include metrics such as the count of NULL values, distinct values, and other data quality indicators. This feature allows users to quickly assess the presence of NULLs without modifying the dataset or adding unnecessary stages.
The other options are not the best approach for this task:
* A. Add a Manage Fields stage: The Manage Fields stage is used to modify field properties (e.g., type, visibility), not to inspect data for NULL values.
* C. Create a Prism calculated field: While a calculated field could be used to flag NULLs (e.g., using ISNULL), this is an indirect and unnecessary step compared to checking stage statistics.
* D. Join on the Worker ID field: Joining with another dataset does not help identify NULL values in the Worker ID field and is irrelevant to this task.
Using stage statistics by clicking on the field name provides a straightforward and efficient way to gain insight into NULL values in the Worker ID field.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Data Quality Checks in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Inspecting Data Using Stage Statistics
質問 # 29
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. As many transformation stages of any type as your scenario requires.
- C. As many transformation stages of any type as long as they are in a particular order.
- D. Up to five transformation stages.
正解:B
解説:
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
質問 # 30
A report that uses a Prism data source that you created has a brand new requirement that Expense Partner by Location should only see expense reports for workers for the locations they support. Given that you have the Location Hierarchy (instance) field in your dataset used for publishing, how can you adjust to meet the requirement?
- A. Run the Edit Data Source Security task to update row-level security.
- B. Run the Edit Data Source Security task to update the Securing Entities.
- C. Run the Edit Data Source Security task to update row-level security and then republish the PDS.
- D. Run the Edit Data Source Security task to update the Securing Entities and then republish the PDS.
正解:C
解説:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, restricting access to specific rows in a Prism data source (PDS) based on afield like Location Hierarchy for users such as Expense Partners requires implementing row-level security.
According to the official Workday Prism Analytics study path documents, to meet the requirement that Expense Partners only see expense reports for the locations they support, you should run the Edit Data Source Security task to update row-level security and then republish the PDS (option B).
The Location Hierarchy (instance) field in the dataset can be used to define row-level security rules, ensuring that each Expense Partner only sees data for their assigned locations. The Edit Data Source Security task allows you to configure row-level security by mapping the Location Hierarchy field to the user's security profile (e.g., their assigned locations). After updating the security settings, republishing the Prism data source is necessary to apply the changes, ensuring the updated security rules take effect in the reports that use the data source.
The other options are incorrect:
* A. Run the Edit Data Source Security task to update the Securing Entities and then republish the PDS:
Securing Entities are used to control overall access to the data source (e.g., via security domains), not to restrict data at the row level based on a field like Location Hierarchy.
* C. Run the Edit Data Source Security task to update row-level security: While updating row-level security is correct, the changes will not take effect in reports until the data source is republished, making this option incomplete.
* D. Run the Edit Data Source Security task to update the Securing Entities: As with option A, updating Securing Entities does not address row-level security for location-based restrictions.
Updating row-level security and republishing the PDS ensures that Expense Partners only see the relevant expense reports, meeting the requirement while maintaining security governance.
References:
Workday Prism Analytics Study Path Documents, Section: Security and Governance in Prism, Topic:
Implementing Row-Level Security in Prism Data Sources
Workday Prism Analytics Training Guide, Module: Security and Governance in Prism, Subtopic: Configuring Row-Level Security for Location-Based Access
質問 # 31
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