Free Practice Questions for Workday Workday-Prism-Analytics Exam

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, window functions are used to perform calculations across a set of rows, such as calculating averages with a function like AVG. According to the official Workday Prism Analytics study path documents, the AVG window function, which computes the average, must operate on a fieldof type Numeric. This is because averaging requires numerical values to perform arithmetic operations (e.g., summing the values and dividing by the count of rows). Non-numeric field types, such as Text or Date, cannot be averaged, and Boolean fields (true/false) are not suitable for this type of calculation. For example, a window function like AVG(salary) OVER (PARTITION BY department) would calculate the average salary per department, where "salary" must be a Numeric field.

The other options are incorrect:

A. Text: Text fields cannot be used for arithmetic operations like averaging.

B. Boolean: Boolean fields (true/false) are not suitable for calculating averages.

D. Date: Date fields cannot be directly averaged; they require conversion to a numeric representation (e.g., days since a reference date) first.

The requirement for a Numeric field type ensures that the AVG window function can perform the necessary mathematical computations accurately.

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.

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.

Comprehensive and Detailed Explanation From Exact Extract:

When importing data into Workday Prism Analytics from a Workday report, including Workday instance field types in the report is critical for enabling specific functionality in the resulting Prism data source. According to the official Workday Prism Analytics study path documents, including instance field types allows the final Prism data source to support drilling into Workday objects. Instance field types represent references to Workday business objects (e.g., Worker, Position, or Organization), and including them in the report ensures that the Prism data source retains the ability to navigate to these objects within Workday’s reporting and analytics framework. This enables users to perform drill-down actions, such as accessing detailed object data directly from Prism visualizations or reports.

The other options do not accurately reflect the primary benefit of including instance field types:

B. Performance is improved in the final Prism datasource when published: Instance field types do not directly impact the performance of the published data source; performance is more influenced by data volume and indexing.

C. Unions are more easily performed with instance field types: Unions depend on schema compatibility, not instance field types, which are specific to Workday object references.

D. Joins are more easily performed with instance field types: While instance field types can be used in joins, their primary purpose is to enable object navigation, not to simplify join operations.

By including instance field types, the Prism data source gains enhanced interactivity, allowing users to leverage Workday’s object model for deeper analysis and navigation.

Comprehensive and Detailed Explanation From Exact Extract:

In Workday Prism Analytics, a published Prism data source (PDS) contains a snapshot of data from a derived dataset at the time of publishing. According to the official Workday Prism Analytics study path documents, for reports using a Prism data source, the data is updated at reimport into tables and republish of the datasource (option B). A derived dataset typically sources data from underlying tables (via import stages), and any updates to the source data require two steps: (1) reimporting the updated data into the tables (e.g., via a Data Change task), and (2) republishing the derived dataset to refresh the Prism data source with the new data. Reports using the PDS will reflect the updated data only after both steps are completed, as the data source is a static snapshot until republished.

The other options are incorrect:

A. At republish of the datasource only: Republishing alone does not update the data if the underlying tables have not been reimported with new data; both steps are necessary.

C. At reimport into tables only: Reimporting into tables updates the source data, but the PDS remains unchanged until the dataset is republished.

D. At report runtime: Reports do not dynamically update the PDS at runtime; they use the data as it exists in the PDS at the time of the last publish.

The combination of reimporting into tables and republishing the data source ensures that reports reflect the most current data.

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.

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.