Pass the Workday Reporting and Analytics Workday-Prism-Analytics Questions and answers with ExamsMirror

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.

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.

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.

In Workday Prism Analytics, joining two tables requires that the fields used in the join condition have compatible data types to avoid data mismatches or loss. The Employee Data TBL has an Employee ID field as text, while the Learner Data TBL has an Employee ID field as numeric. According to the official Workday Prism Analytics study path documents, to join these tables without potential data loss, the best approach is to convert the numeric Employee ID in the Learner Data TBL to text, as text fields can safely store numeric values as strings, but converting text to numeric risks data loss if the text field contains non-numeric characters (e.g., leading zeros or special characters).

The correct method is to import the Learner Data TBL into a Derived Dataset (DDS) and use a Manage Fields stage to change the field type of Employee ID from numeric to text (option D). This ensures that the Employee ID field in both tables is text, enabling a safe and accurate join without losing data. The Manage Fields stage in a DDS allows for field type transformations, which is the recommended approach for preparing data for joins in Prism Analytics.

The other options are less suitable:

A. Import the Employee Data TBL into a DDS and change the field type of Employee ID from text to numeric using a Manage Fields stage: Converting text to numeric risks data loss if the text field contains non-numeric values, which could lead to errors or missing records during the join.

B. Change the field type of Employee ID directly on the Employee Data TBL from text to numeric: Direct field type changes on tables are not supported in Prism Analytics, and even if possible, this approach risks data loss for the same reason as option A.

C. Change the field type of Employee ID directly on the Learner Data TBL from Numeric to Text: Direct field type changes on tables are not supported; field type transformations must be done in a DDS using a Manage Fields stage.

By converting the numeric Employee ID to text in a DDS, the join can be performed safely, preserving all data from both tables.

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.

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, hiding a field is a common practice to control access to sensitive information while still allowing necessary analytics to be performed. According to the official Workday Prism Analytics study path documents, the primary reason for hiding a field like employee salary information is to protect sensitive data. Employee salary is considered personally identifiable information (PII) or sensitive data, and hiding the field ensures that individual salary details are not exposed to unauthorized users or in published data sources. However, by hiding the field, Prism data writers can still use it in calculations—such as computing the average salary by cost center—because hidden fields remain accessible for transformation and aggregation purposes within the dataset but are not visible in the final output or to end users of the published data source.

The other options do not align with the scenario:

B. To hide Prism-calculated fields used for interim processing: The salary field is a base field, not a calculated field used for interim processing, so this reason does not apply.

C. To hide unpopulated or sparse data fields: There is no indication that the salary field is unpopulated or sparse; the concern is about its sensitivity, not its data quality.

D. To use computed values instead of base values: Hiding the field does not inherently involve replacing it with computed values; the goal is to restrict visibility while still allowing computations like averages.

Hiding the salary field protects sensitive data while enabling aggregated analytics, aligning with Prism’s security and governance capabilities.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, the TO_DATE function in a calculated field is used to convert a string or compatible data type into a date. However, in this scenario, the field from Workday is numeric, and the TO_DATE function typically requires a string input (e.g., a numeric value like 20230101 needs to be converted to a string like "20230101" before applying TO_DATE). According to the official Workday Prism Analytics study path documents, to prepare the numeric field for use with the TO_DATE function, you must first use a Manage Fields stage to change the field’s Output Type to Text. The Manage Fields stage allows you to modify the field’s properties, and changing the Output Type from Numeric to Text converts the numeric values into a string format that the TO_DATE function can then process (e.g., TO_DATE([Field_Name], "YYYYMMDD")).

The other options are not relevant:

B. Output Name: Changing the Output Name renames the field but does not address the field type compatibility required for the TO_DATE function.

C. Input Type: The Manage Fields stage does not modify an "Input Type"; it adjusts the Output Type to transform the field as it moves through the pipeline.

D. Input Name: There is no "Input Name" property in the Manage Fields stage; this option is not applicable.

By changing the Output Type to Text in the Manage Fields stage, the numeric field is converted to a string, making it compatible with the TO_DATE function for creating a date field in the calculated field.

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.