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NEW QUESTION # 29
What security domain enables the ability to create a dataset?
- A. Prism: Manage Data Source
- B. Prism Datasets: Create
- C. Prism Datasets: Publish
- D. Prism: Tables Create
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the ability to create a dataset is governed by specific security domains within the Prism Analytics functional area. According to the official Workday Prism Analytics study path documents, the security domain that explicitly enables users to create datasets is the "Prism Datasets: Create" domain.
This domain grants users the necessary permissions to initiate the creation of both base and derived datasets within the Prism Analytics Data Catalog.
The process of creating a dataset involves defining the metadata and processing logic to manipulate data, which can include Workday or external (non-Workday) data sources. The "Prism Datasets: Create" domain ensures that only authorized users, such as Prism data analysts or administrators, can perform this task, aligning with Workday's configurable security framework. Other domains, such as "Prism Datasets: Publish," are responsible for publishing datasets to make them available as Prism Data Sources for reporting, while
"Prism: Manage Data Source" pertains to managing the data sources themselves, not creating datasets.
Similarly, "Prism: Tables Create" is related to creating tables, which is a distinct entity from datasets in Prism Analytics.
This distinction is critical, as datasets and tables serve different purposes in the data management workflow.
Datasets include metadata and a subset of example rows, while tables contain metadata and all data rows. The
"Prism Datasets: Create" domain is specifically designed to control access to dataset creation, ensuring secure and governed data preparation.
References:
Workday Prism Analytics Study Path Documents, Section: Security and Governance in Prism Analytics, Topic: Security Domains and Permissions Workday Prism Analytics Training Guide, Module: Datasets and Data Sources, Subtopic: Creating Datasets and Associated Security
NEW QUESTION # 30
What window function returns the number of rows within a window?
- A. COUNT
- B. AVG
- C. MAX
- D. SUM
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, window functions are used to perform calculations over a defined set of rows (a
"window"). According to the official Workday Prism Analytics study path documents, the COUNT window function is used to return the number of rows within a specified window. When applied in a dataset transformation, the COUNT function counts the rows that fall within the window, which can be defined by partitioning (e.g., by a specific column) and ordering criteria. For example, COUNT(*) OVER (PARTITION BY department) would return the number of rows for each department in the dataset.
The other options serve different purposes:
A: MAX: Returns the maximum value within the window, not the number of rows.
B: SUM: Calculates the sum of a numeric field across the window, not the row count.
D: AVG: Computes the average of a numeric field within the window, not the row count.
The COUNT function is specifically designed to provide the row count, making it the correct choice for this purpose in Prism Analytics transformations.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Window Functions and Their Applications Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using COUNT in Window Functions
NEW QUESTION # 31
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 Join stage with a Right Outer Join.
- C. Add a Union stage.
- D. Add a Join stage with a Left Outer Join.
Answer: C
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 # 32
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. Union
- B. Group By
- C. Manage Fields
- D. Filter
Answer: B
Explanation:
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
NEW QUESTION # 33
What is the primary purpose of window functions in Prism?
- A. To provide row-level access control.
- B. To filter rows based on specified conditions.
- 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 # 34
What is a feature of using an sFTP connection on a data change task?
- A. You can copy sFTP connections.
- B. You can select multiple target tables in the data change task.
- C. You can reuse an sFTP connection in multiple data change tasks.
- D. You can import an XLSX file from an sFTP server.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a secure File Transfer Protocol (sFTP) connection can be configured as a source for a Data Change task to import data into a table. According to the official Workday Prism Analytics study path documents, a key feature of using an sFTP connection is that it can be reused across multiple Data Change tasks. Once an sFTP connection is defined in the Prism Analytics environment, it is stored and can be selected as the source connection for different Data Change tasks, promoting efficiency and consistency in data ingestion workflows. This reusability reduces the need to redefine connection parameters for each task, streamlining the configuration process.
The other options are not accurate:
* A. You can copy sFTP connections: While connections can be managed, there is no specific feature in Prism Analytics to "copy" sFTP connections as a distinct action.
* C. You can import an XLSX file from an sFTP server: While sFTP connections support various file formats (e.g., CSV), the ability to import XLSX files is not guaranteed and depends on the system's configuration, making this option less definitive.
* D. You can select multiple target tables in the data change task: A Data Change task is designed to load data into a single target table, not multiple tables simultaneously, regardless of the connection type.
The ability to reuse an sFTP connection across multiple Data Change tasks is a core feature that enhances the flexibility and scalability of data import processes in Prism Analytics.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Configuring Data Change Tasks with sFTP Connections Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Managing Source Connections for Data Ingestion
NEW QUESTION # 35
When joining datasets, what items must match?
- A. The number of rows in each dataset.
- B. The field types for the Match Row fields.
- C. The field names for the Match Row fields.
- D. The level of detail in each dataset.
Answer: B
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 # 36
You are asked to produce a Prism data source, which is going to be used in a matrix report that should display the minimum, maximum, total, average, and the median purchase order amount by location and month. What should you do to achieve the desired result?
- A. Add two Group By stages to your derived dataset.
- B. Publish your detail data and build the summarizations in the advanced report.
- C. Publish your detail data and build the summarizations in the matrix report.
- D. Add a Group By stage to your derived dataset.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a matrix report in Workday is designed to display summarized data in a grid format, with built-in capabilities to calculate aggregations like minimum, maximum, total, average, and median. According to the official Workday Prism Analytics study path documents, to produce a Prism data source for a matrix report that needs to display the minimum, maximum, total, average, and median purchase order amount by location and month, you should publish your detail data and build the summarizations in the matrix report (option A).
Publishing the detail data (i.e., the raw purchase order data with fields like location, month, and amount) as a Prism data source allows the matrix report to access the granular data. The matrix report can then apply the required aggregations (MIN, MAX, SUM, AVG, MEDIAN) directly, grouping by location and month as specified in the report configuration. This approach leverages Workday's reporting capabilities, reducing the need for additional transformations in Prism and ensuring flexibility for future reporting needs.
The other options are less efficient:
* B. Add a Group By stage to your derived dataset: A Group By stage in the derived dataset can compute some aggregations (e.g., SUM, AVG), but Prism does not natively support calculating the median in a Group By stage, and it would require multiple stages or calculated fields to compute all metrics, making it less practical than using the matrix report.
* C. Publish your detail data and build the summarizations in the advanced report: While an advanced report can perform some summarizations, it is not as well-suited as a matrix report for displaying multiple aggregations (like median) in a grid format by location and month.
* D. Add two Group By stages to your derived dataset: Using two Group By stages is unnecessary and still does not address the limitation of calculating the median in Prism, making this approach overly complex.
Publishing the detail data and letting the matrix report handle the summarizations is the most efficient and effective way to meet the requirements.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic: Preparing Data for Matrix Reports Workday Prism Analytics Training Guide, Module: Integrating Prism with Workday Reports, Subtopic:
Leveraging Matrix Reports for Aggregations
NEW QUESTION # 37
The final derived dataset in a Prism pipeline is complete and ready to publish. What should be done prior to publishing?
- A. Edit the Dataset API Name to reflect in the name of the Prism data source.
- B. Create a derived dataset with the PDS suffix.
- C. Add a Group By stage to the final derived dataset to add summary calculations.
- D. Create a table without the Enable for Analysis checkbox selected.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, before publishing a derived dataset as a Prism data source (PDS), it's important to ensure that the dataset is properly configured for downstream use. According to the official Workday Prism Analytics study path documents, one key step to take prior to publishing is to edit the Dataset API Name to reflect in the name of the Prism data source (option D). The Dataset API Name determines the name of the published Prism data source, which is used in reporting, discovery boards, and integrations. Setting a meaningful and descriptive API name (e.g., "Expense_Reports_by_Location") ensures that the data source is easily identifiable and aligns with naming conventions, improving usability and manageability in the Workday ecosystem. This step is a best practice to avoid confusion and ensure clarity for report writers and analysts.
The other options are not required or relevant:
* A. Add a Group By stage to the final derived dataset to add summary calculations: Adding a Group By stage is not mandatory unless the use case specifically requires summarizations, which is not indicated here.
* B. Create a table without the Enable for Analysis checkbox selected: Creating a new table is unnecessary, as the dataset is already complete, and the "Enable for Analysis" checkbox is relevant for real-time updates, not a requirement for publishing a derived dataset.
* C. Create a derived dataset with the PDS suffix: Creating a new dataset is not needed, as the final derived dataset is already prepared, and adding a "PDS" suffix is not a required step for publishing.
Editing the Dataset API Name ensures the Prism data source has a clear and meaningful name, facilitating its use in reporting and analytics.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic: Best Practices for Publishing Prism Data Sources Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Configuring Dataset API Names Before Publishing
NEW QUESTION # 38
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. As many transformation stages of any type as your scenario requires.
- B. Up to two Manage Fields transformation stages.
- C. Up to five transformation stages.
- D. As many transformation stages of any type as long as they are in a particular order.
Answer: A
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 # 39
You accidentally delete a Prism calculated field that is used in other Prism calculated fields or conditions.
What is a possible outcome?
- A. Any calculated field referencing the deleted field defaults to zero.
- B. Errors will result in any stage or calculated field that references the field.
- C. The system will automatically adjust any dependencies accordingly.
- 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 # 40
You have to blend two sources of data. Your matching field is Employee ID, which is a text-type field in Pipeline 1, but is numeric in Pipeline 2. How do you prepare your data for blending?
- A. Add first a Manage Fields to change the field type and then Join.
- B. Add a Filter first and then a Manage Fields to change the field type.
- C. Add a Join first and then a Manage Fields to change the field type.
- D. Add a Manage Fields to change the field type and then Union.
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, blending two data sources typically involves joining them on a common field, such as Employee ID in this case. However, the Employee ID field is text in Pipeline 1 and numeric in Pipeline 2, which means the field types must be aligned before a join can be performed to avoid data mismatches or errors. According to the official Workday Prism Analytics study path documents, the correct approach is to first use a Manage Fields stage to change the field type of Employee ID in one of the pipelines to match the other (e.g., convert the numeric Employee ID in Pipeline 2 to text, as text can safely store numeric values without data loss), and then perform a Join stage to blend the data (option C). Converting from numeric to text is preferred because converting text to numeric risks data loss if the text field contains non- numeric characters.
The other options are not appropriate:
* A. Add a Manage Fields to change the field type and then Union: A Union appends rows vertically and does not blend data based on a matching field like Employee ID; blending typically requires a Join.
* B. Add a Filter first and then a Manage Fields to change the field type: Adding a Filter stage is unnecessary for preparing the field types for a join and does not address the blending requirement.
* D. Add a Join first and then a Manage Fields to change the field type: Performing the Join first will fail or produce incorrect results because the field types (text and numeric) are incompatible for joining; the types must be aligned before the Join.
By using a Manage Fields stage to change the field type first and then performing a Join, the data from both pipelines can be blended accurately on the Employee ID field.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Preparing Data for Joins in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Field Type Transformations for Data Blending
NEW QUESTION # 41
An HR analyst is tasked to create custom reports for their company's performance reviews. The analyst uses both Workday and Prism for data analysis. What Workday-calculated field functions would the analyst be able to build off of their Prism object?
- A. Extract Single Instance
- B. Lookup Field with Prompts
- C. Lookup Related Value
- D. Arithmetic Calculation
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when integrating with Workday reports, a Prism object (i.e., a published Prism data source) can be used as the basis for custom reports, and certain Workday-calculated field functions can be applied to it. According to the official Workday Prism Analytics study path documents, the Arithmetic Calculation function (option B) is a supported Workday-calculated field function that can be built off a Prism object. This function allows the analyst to perform mathematical operations (e.g., addition, subtraction, multiplication) on numeric fields within the Prism data source, such as calculatinga performance review score by combining multiple metrics. Since Prism data sources often contain pre-processed data, arithmetic calculations can be applied to enhance the data for reporting purposes.
The other options are not supported for Prism objects:
* A. Extract Single Instance: This function is used to extract a single instance from a multi-instance field in Workday, but Prism objects typically contain single-instance fields after transformations (e.g., via an Explode stage), making this function inapplicable.
* C. Lookup Related Value: This function retrieves related values from other Workday business objects, but Prism objects do not support direct lookups to Workday objects in this manner; such relationships must be pre-built in the Prism dataset.
* D. Lookup Field with Prompts: This function involves interactive prompting, which is not supported for Prism objects in Workday reports, as Prism data sources are static snapshots of data.
The Arithmetic Calculation function provides the flexibility to perform numerical computations on Prism data, making it a suitable choice for enhancing performance review reports.
References:
Workday Prism Analytics Study Path Documents, Section: Integrating Prism with Workday Reports, Topic:
Using Calculated Fields with Prism Objects
Workday Prism Analytics Training Guide, Module: Integrating Prism with Workday Reports, Subtopic:
Supported Calculated Field Functions for Prism Data Sources
NEW QUESTION # 42
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. Edit the data source security and select a domain.
- B. Share the imported Workday report to provide users with access to the published Prism data source.
- C. Schedule the recurring publish process.
- D. Share the dataset with appropriate users.
Answer: A
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 # 43
A Prism data administrator is ready to create a Prism data source. As data is updated in Prism, the goal is to update the data in the Prism data source concurrently, enabling immediate incremental updates. How should the administrator create the Prism data source?
- A. Set Data Source Security on a derived dataset and select Publish.
- B. Publish a derived dataset with the Prism: Default to Dataset Access Domain.
- C. Create a table and select Publish.
- D. Create a table and select the Enable for Analysis checkbox.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, creating a Prism data source that supports immediate incremental updates as data is updated in Prism requires a specific configuration. According to the official Workday Prism Analytics study path documents, the administrator should create a table and select the Enable for Analysis checkbox (option A). The "Enable for Analysis" option, when selected during table creation, allows the table to be used directly as a Prism data source with real-time updates. This setting ensures that as data in the table is updated (e.g., through a Data Change task), the changes are immediately reflected in the Prism data source, enabling incremental updates without the need for republishing. This is particularly useful for scenarios requiring near- real-time data availability in reporting or analytics.
The other options do not achieve the goal of immediate incremental updates:
* B. Create a table and select Publish: Publishing a table creates a static Prism data source, but updates to the table require republishing, which does not support immediate incremental updates.
* C. Publish a derived dataset with the Prism: Default to Dataset Access Domain: Publishing a derived dataset creates a data source, but updates to the underlying data require republishing the dataset, which is not concurrent or incremental.
* D. Set Data Source Security on a derived dataset and select Publish: Setting security and publishing a derived dataset follows the same process as option C, requiring republishing for updates, which does not meet the requirement for immediate updates.
Selecting the "Enable for Analysis" checkbox when creating a table ensures the Prism data source supports concurrent, incremental updates as data changes in Prism.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic: Creating Prism Data Sources with Real-Time Updates Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Configuring Tables for Incremental Updates
NEW QUESTION # 44
You are adding a Join stage and choose Join type of Left Outer Join, causing Workday to search for a matching row in the imported pipeline. What happens if no matching rows exist?
- A. Included fields from both pipelines will have NULL values.
- B. A duplicate row will be generated.
- C. The row will be omitted.
- D. Included fields from the imported pipeline will have NULL values.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Left Outer Join in a Join stage includes all rows from the primary pipeline (the left pipeline) and attempts to match them with rows from the imported pipeline (the right pipeline) based on the join condition. According to the official Workday Prism Analytics study path documents, if no matching rows exist in the imported pipeline for a given row in the primary pipeline, the row from the primary pipeline is still included in the output, but the fields from the imported pipeline will have NULL values. This behavior ensures that all data from the primary pipeline is retained, while the absence of a match in the imported pipeline is represented by NULLs for the corresponding fields.
The other options are incorrect:
* A. A duplicate row will be generated: A Left Outer Join does not generate duplicate rows; duplicates would occur only if multiple matches exist in the imported pipeline, which is not the case here.
* B. The row will be omitted: In a Left Outer Join, rows from the primary pipeline are never omitted, even if no match is found; this behavior is specific to an Inner Join.
* D. Included fields from both pipelines will have NULL values: Only the fields from the imported pipeline will have NULL values; the fields from the primary pipeline retain their original values.
This behavior of Left Outer Join ensures that all records from the primary pipeline are preserved, with NULLs indicating the absence of matching data from the imported pipeline.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Join Types and Their Behaviors in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Join Stages in Derived Datasets
NEW QUESTION # 45
You want to use a custom report containing prompts as a source connection for a table. What must you ensure to make this possible?
- A. The prompts are mapped at the data change task level.
- B. The prompts are marked as required.
- C. The custom report prompts have default values assigned on the report definition.
- D. The report is built on an indexed data source.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when using a custom report with prompts as a source connection for a table, the custom report must be configured to ensure compatibility with the Prism data ingestion process. According to the official Workday Prism Analytics study path documents, the key requirement is that the custom report prompts have default values assigned in the report definition. This is necessary because Prism Analytics does not support interactive prompting during data ingestion. Default values ensure that the report can run automatically without requiring user input, allowing the Data Change task to retrieve the data consistently and load it into the target table.
The other options are not correct in this context:
* A. The report is built on an indexed data source: While indexed data sources can enhance performance for certain reports, they are not a requirement for using a custom report as a source for a Prism table.
* B. The prompts are mapped at the data change task level: Prompts are not mapped in the Data Change task; instead, the task relies on the report's default values to execute the data retrieval.
* D. The prompts are marked as required: Marking prompts as required does not address the need for automatic execution; default values are still needed to avoid manual intervention.
By assigning default values to prompts in the custom report definition, the report can be seamlessly integrated as a source connection for Prism Analytics, ensuring reliable data loading into the table.
References:
Workday Prism Analytics Study Path Documents, Section: Integrating Prism with Workday Reports, Topic:
Using Custom Reports as Data Sources
Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Custom Reports for Prism Integration
NEW QUESTION # 46
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. Inner 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 # 47
What task or report should you access to view a Prism data source?
- A. View Dataset Details report
- B. Edit Dataset Transformations task
- C. View Prism Data Source report
- D. Edit Data Source Security task
Answer: C
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 # 48
A Prism data writer has to create an intermediary Prism calculated field A, used only to achieve a final result in Prism calculated field B and they only need to publish out field B. What should they do?
- A. Add a Manage Fields stage to the DDS and hide field B.
- B. Mark field A as intermediate calculation.
- C. Delete field A from their DDS and just leave field B.
- D. Add a Manage Fields stage to the DDS and hide field A.
Answer: D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when a data writer creates an intermediary calculated field (e.g., field A) solely to derive a final calculated field (e.g., field B) in a Derived Dataset (DDS), they may want to exclude the intermediary field from the published output to keep the dataset clean and focused. According to the official Workday Prism Analytics study path documents, the recommended approach is to add a Manage Fields stage to the DDS and hide field A. The Manage Fields stage allows users to control the visibility of fields in the dataset, enabling them to hide fields that are not needed in the final output while retaining their calculations for internal use within the dataset's transformation logic. By hiding field A, field B can still leverage field A's calculations, and only field B will be visible in the published dataset or data source.
The other options are not suitable:
A: Mark field A as intermediate calculation: There is no specific feature in Prism Analytics to "mark" a field as an intermediate calculation; this is not a supported action.
C: Add a Manage Fields stage to the DDS and hide field B: Hiding field B would defeat the purpose, as field B is the intended output to be published.
D: Delete field A from their DDS and just leave field B: Deleting field A would break the calculation of field B, as field B depends on field A, making this option infeasible.
Using the Manage Fields stage to hide field A ensures that the dataset remains functional while presenting only the necessary fields in the final output, aligning with best practices for data transformation and publishing.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Managing Fields in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Configuring Field Visibility in Datasets
NEW QUESTION # 49
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. Create a derived dataset and build a CASE calculated field to classify workers against their pay.
- B. Build a CASE calculated field function on the TBL directly to ease later transformation.
- C. Build an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay.
- D. Add the additional field to your raw data before you ingest into Prism.
Answer: A
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 # 50
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