Free WORKDAY-PRISM-ANALYTICS Practice Test

In Workday, access to all data sources, including those published from Prism Analytics, is governed by the domain security framework. To make a published Prism data source available for use in reporting tools like Report Writer or discovery boards, an administrator must explicitly assign it to a securable domain. Security groups (e.g., Role-Based or User-Based) are then granted permissions to access that domain. This action directly controls which users can see and build content from the data source, ensuring proper data governance and access control.

B. Share the dataset with appropriate users. Sharing a dataset in Prism is for collaboration among data analysts to edit the data preparation logic, not for granting end-user access to the published data source for reporting.
C. Share the imported Workday report to provide users with access to the published Prism data source. The published Prism data source is a distinct object from its original source (e.g., a Workday report). Securing the original source does not confer security permissions to the new Prism object.
D. Schedule the recurring publish process. Scheduling a publish process is an operational step to make data available and keep it refreshed. It does not configure the security permissions that determine who can access the data.

1. Workday, Inc. (2023). Prism Analytics: Administration and Security.

Section: "Securing Prism Data Sources"

Content: This official guide explicitly states, "After you publish a dataset, the resulting data source is an unsecured object. To enable users to access the data source for reporting and analytics, you must edit the data source's security and add it to a securable domain. You can then configure the domain's security policy to grant access to the appropriate security groups." This directly validates that editing the data source security to select a domain is the required action.

2. Workday, Inc. (2023). Workday Security Administration Handbook.

Section: "Domain Security for Custom and Prism Data Sources"

Content: The documentation details that all reportable data sources must belong to a domain to be accessible. It outlines the process: "For any new data source, including those generated by Prism Analytics, navigate to the 'View Data Source' task, select the 'Security' action, and assign the data source to a relevant domain, such as 'Prism Analytics Data Sources'. Without this assignment, the data source remains invisible to all users for reporting purposes."

The requirement is to "append" historical worker data to the existing current worker dataset. In data transformation, appending means adding rows from one dataset to another, effectively stacking them vertically. The Union stage in Workday Prism Analytics is specifically designed for this operation. It combines rows from two or more datasets into a single dataset. The Union stage also creates a unified schema, which accommodates datasets where columns do not perfectly align by including all columns from all inputs and populating non-matching fields with null values, thus creating the required "uniform layout."

A. A Join stage with a Right Outer Join combines datasets horizontally by adding columns based on a key, not by appending rows vertically.
C. A Join stage with a Left Outer Join also combines datasets horizontally by adding columns, not by appending rows.
D. A Join stage with an Inner Join combines datasets horizontally and only retains rows with matching keys in both datasets, which is not the goal here.

1. Workday Prism Analytics Documentation, "Prepare Data". Section: Union Stage. This section details how the Union stage is used to combine rows from multiple datasets. It explicitly states, "Use a Union stage to combine rows from 2 or more datasets into a single dataset... The output dataset contains all columns from all input datasets." This directly supports the use of Union for appending data with potentially misaligned columns.

2. Workday Prism Analytics Documentation, "Prepare Data". Section: Join Stage. This documentation describes the function of a Join stage as combining columns from two datasets based on a common key. It outlines the different join types (Inner, Left Outer, Right Outer, Full Outer), all of which perform horizontal data merging, contrasting with the vertical merging (appending) required by the question.

3. Workday Training Courseware, "Prism Analytics: Data Preparation and Transformation". Module: Combining Datasets. This training material differentiates between joining and unioning data. It presents a use case for the Union stage that mirrors the question's scenario: combining datasets with similar structures but from different time periods (e.g., Q1 sales and Q2 sales) into a single, comprehensive dataset.

Window functions in Workday Prism Analytics are used to perform calculations on a specific subset of rows, known as a "window" or "partition," that are related to the current row being processed. Unlike standard aggregate functions which collapse rows, window functions return a value for each row based on the calculation over its window. This enables complex analytics such as calculating running totals, moving averages, or ranking records within specific categories (e.g., ranking employees by sales within each region) without losing the detail of the original rows.

A. To provide row-level access control. This is incorrect. Row-level access is a security feature managed through Workday's security framework, not a data transformation function within a Prism pipeline.
B. To manipulate strings and dates within a query. This is incorrect. While window functions can operate on date or string fields for partitioning or ordering, their primary purpose is calculation over a set of rows, not basic manipulation, which is handled by scalar functions.
C. To filter rows based on specified conditions. This is incorrect. Filtering is the primary purpose of a Filter stage or a WHERE clause. While the output of a window function can be used as a criterion for filtering, the function itself performs a calculation.

1. Workday Prism Analytics Documentation, "Expression Language in Prism Analytics": This guide details the available functions for data transformation. It describes window functions as a category that "performs a calculation across a set of table rows that are somehow related to the current row." It provides examples like RANK(), ROWNUMBER(), and aggregate functions used with an OVER (PARTITION BY ...) clause, which directly supports the concept of performing calculations across a partitioned set of rows. (Accessed via Workday Community, specific document ID varies by version).

2. Workday Prism Analytics Training Manual, "Data Preparation and Transformation" module: In the section on the "Transform Data" stage, the manual explains that window functions are essential for creating calculations that depend on a group of records, such as period-over-period analysis or ranking, without aggregating the dataset. It emphasizes the use of PARTITION BY to define the set of rows for the calculation, aligning perfectly with the correct answer. (Found in official Workday Learning courseware for Prism Analytics certification).

The central requirement is to display the median purchase order amount in the final matrix report. Standard Workday matrix reports do not have a built-in summary function to calculate the median; they are limited to aggregations like Sum, Average, Count, Minimum, and Maximum. Therefore, the median and other required aggregations (min, max, total, average) must be calculated before the data is used in the report. The correct method is to use a Group By stage within the Prism derived dataset. This stage can group the detailed purchase order data by location and month and apply multiple aggregation functions, including Median, to generate the required summarized values. The resulting summarized dataset is then published and used as the data source for the matrix report.

A. This is incorrect because a standard matrix report cannot calculate the median. Publishing detail data would fail to meet this specific requirement.
C. This is incorrect because the question explicitly states the output should be a matrix report, which is designed for multi-dimensional analysis, not an advanced report.
D. This is incorrect because a single Group By stage is the correct and efficient way to group by multiple fields (location and month) simultaneously.

1. Workday Prism Analytics Documentation, "Prepare and Blend Data": The section on the Group By stage in derived datasets explicitly lists "Median" as an available aggregation function. This confirms that complex statistical calculations not available in the standard report writer can be performed within the Prism data preparation layer. (Reference: Workday Community, Prism Analytics: Prepare and Blend Data, Section: "Group By Stage Aggregations").

2. Workday Report Writer Documentation, "Create a Matrix Report": The documentation detailing the configuration of summary fields and data columns in matrix reports lists the available summary operations as Sum, Count, Count Distinct, Average, Minimum, and Maximum. Median is not included in this list, confirming its calculation is not a native capability of the reporting tool. (Reference: Workday Community, Report Writer Reference Guide, Section: "Configuring Matrix Report Summaries").

3. Workday Courseware, "Prism Analytics: Advanced Topics": Course materials often highlight the design pattern of leveraging Prism's advanced transformation capabilities to pre-aggregate data. This simplifies the final report build and enables the use of calculations, like median or standard deviation, that are beyond the scope of the standard reporting tools. The guidance is to perform complex calculations in Prism and use reporting tools for visualization of the prepared data. (Reference: Workday Learning, Prism Analytics Course Manual, Module: "Optimizing Data for Reporting").

In Workday Prism Analytics, a derived dataset is constructed by creating a pipeline of sequential transformation stages. After the initial data import stage (Stage 1), you can add numerous transformation stages to clean, shape, blend, and enrich the data. The platform is designed to be flexible, allowing users to add as many stages of any available type (e.g., Filter, Join, Group & Aggregate, Manage Fields) as necessary to meet the specific requirements of their data preparation scenario. There is no predefined limit on the number of stages.

B. The order of stages is critical and defined by the user's logic, but there is no mandated "particular order" for different stage types.
C. There is no documented system limitation of only five transformation stages. Complex data preparation often requires more stages.
D. There is no specific limit on how many times a 'Manage Fields' stage can be used within a single derived dataset pipeline.

1. Workday Prism Analytics: Prepare and Blend Data Guide: The guide details the process of creating derived datasets. In the section "Derived Dataset Editor," it describes how to add stages sequentially to a pipeline. The documentation illustrates adding various stage types (Filter, Join, Group & Aggregate, etc.) one after another, without mentioning any numerical cap, which supports the principle of adding as many as the scenario requires. (Reference: Workday Community, "Workday Prism Analytics: Prepare and Blend Data," Section: Derived Dataset Editor).

2. Workday Prism Analytics: Reference Guide: This document lists and describes all available transformation stage types. The descriptions focus on the function of each stage and how to configure it, implying they can be combined in any logical sequence and number needed to achieve the desired outcome. There is no mention of limitations on the quantity or order of stage types. (Reference: Workday Community, "Workday Prism Analytics: Reference Guide," Section: Transformation Stages).

The user's request requires filtering data based on two criteria that must both be true for each record: the submission date must be in the current month, AND the total amount must be greater than 2000 USD. In Workday Prism Analytics, this is achieved using a Filter stage. You would define two separate conditions: one for the date and one for the amount. The "If All" operator functions as a logical AND, ensuring that only rows satisfying all specified conditions are retained in the dataset. This configuration precisely matches the stated requirement.

B. The "If Any" operator functions as a logical OR. It would incorrectly include reports from the current month regardless of amount, or reports over 2000 USD regardless of date.
C. The scenario describes only two distinct filtering criteria (date and amount). Using three conditions is unnecessary and does not align with the problem statement.
D. This option is incorrect for two reasons: it specifies the wrong number of conditions (three instead of two) and the wrong logical operator ("If Any" instead of "If All").

1. Workday Prism Analytics: Prepare Data (Official Documentation). In the section detailing data preparation stages, the "Filter Stage" is described. The documentation specifies that when multiple conditions are added to a filter, the user must select a logical operator. The "If All" option is explicitly defined as requiring a row to meet every condition to be included (AND logic), while "If Any" requires a row to meet at least one condition (OR logic). This directly supports using "If All" for the "and" requirement in the question. (Reference found in Workday Community documentation, specific document/page may vary by tenant version).

2. Workday Prism Analytics: Training Manual (University/Internal Training Courseware). Module on "Data Transformation in Derived Datasets". The lab exercise on filtering data demonstrates creating a filter with multiple conditions. It explains that to combine conditions with an "AND" relationship, the "If All" radio button must be selected. This aligns with the scenario of needing both the date and amount conditions to be met. (Reference from typical Workday-sanctioned training materials).

Window functions in Workday Prism Analytics perform calculations across a set of table rows. The AVERAGE function specifically calculates the arithmetic mean of a group of values. By its mathematical definition, an average is computed by summing a set of numbers and dividing by the count of those numbers. Therefore, the function must operate on a field with a Numeric data type (such as integer or decimal) to perform the required addition and division operations successfully.

A. Text: Mathematical operations like summation, which are required to calculate an average, cannot be performed on text strings.
B. Boolean: The AVERAGE function is not designed to operate on logical True/False values; it requires quantitative data.
D. Date: While specialized functions can find a central point in time, the standard AVERAGE function is intended for numeric values, not date fields.

1. Workday, Inc. (2023). Workday Prism Analytics: Prepare Data. Section: Add a Window Function Step.

This official guide details the configuration of the Window transformation step. In the documentation for the AVG() function, it specifies that the expression or column provided as an argument must be of a numeric data type.

2. Workday, Inc. (2023). Prism Analytics: Reference for PQL Functions. Section: Aggregate Functions.

The reference for Prism Query Language (PQL) explicitly defines the syntax and data type requirements for its functions. The AVG(numericexpression) function is documented as accepting only numeric arguments to return their average value.

Deleting the published rows from a Workday Prism Analytics data source is a data management operation that removes the data records but does not affect the data source's metadata (i.e., its schema, fields, and structure). Custom reports are built upon this metadata definition. Therefore, the report's definition remains completely intact and valid. The report will simply return no results until the corrected data is published again to the source, at which point it will function correctly without any required changes.

B. The report definition will need to be manually recreated. This is incorrect. Deleting data is not a destructive action on the report's metadata; the definition is preserved.
C. The report definition will be copied and a new version will appear after republishing. This is incorrect. Workday does not automatically create new versions or copies of report definitions when the underlying data is managed.
D. The report definition will need to be edited to reflect changes. This is incorrect. Editing is only necessary if the schema of the data source changes (e.g., a field is removed), not when only the data rows are deleted.

1. Workday Prism Analytics Documentation, "Manage Data in a Prism Data Source" section. This section details the available actions for a published data source, including "Delete Published Data." The documentation clarifies that this action affects only the data instance, not the data source definition or dependent objects like reports. The report definition remains linked to the unchanged data source schema.

2. Workday Prism Analytics Documentation, "Create a Custom Report on a Prism Data Source" section. This guide explains that a custom report's structure is tied to the fields available in the data source. As long as the fields remain the same, the report definition is valid, regardless of whether the data source currently contains zero or millions of rows.

Prism import and publish jobs are executed as background processes within the Workday tenant. The Scheduled Future Processes report is the standard, system-wide tool in Workday used to view, manage, and cancel any background process that is scheduled to run in the future. This includes recurring Prism data ingestion and publishing tasks. Users with the appropriate permissions can use this report to find a specific Prism job and modify its schedule or cancel future runs, directly addressing the need to "edit and maintain" these schedules.

B. Prism Management Console: This is not a standard, single report in Workday. Prism management is accomplished through a collection of different reports and tasks, not one centralized console.
C. Prism Activities Monitor: This report is used for monitoring the real-time status and history of Prism jobs (e.g., completed, failed, in progress), not for editing or managing future schedules.
D. Prism Usage: This report provides metrics and analytics on data source consumption, storage size, and user activity. It is used for governance and resource management, not for scheduling processes.

1. Workday Prism Analytics Administrator Guide: In the section on "Managing Prism Pipelines," the documentation specifies that once a pipeline is scheduled, its future runs are managed like any other background process. It directs administrators to the Scheduled Future Processes report to view, modify, or cancel these scheduled tasks. (Reference: Workday Prism Analytics: Administration and Security, Chapter 4: Data Integration and Publishing, Section: "Scheduling and Monitoring Prism Processes").

2. Workday Core HCM Documentation: The functionality of the Scheduled Future Processes report is detailed in the core platform documentation. It is described as the central location for administrators to manage all scheduled reports, integrations, and other background tasks across the tenant, which inherently includes Prism jobs. (Reference: Workday System Administration Guide, Section: "Process Monitoring Tools").

Advanced filter logic in Workday Prism Analytics is specifically required when you need to construct complex filter conditions that involve a combination of AND and OR operators. Basic filter logic implicitly joins all conditions with an AND operator. To create more nuanced filtering, such as (Condition A AND Condition B) OR Condition C, the configurator must switch to the advanced logic view to manually define the relationship between the different filter criteria using parentheses and the appropriate logical operators.

A. Removing NULL values is a fundamental filtering operation that can be accomplished using basic filter logic with operators like "is empty" or "is not empty".
B. Operators such as "equal to" or "not equal to" are standard comparison operators available within the basic filter logic interface.
C. Operators like "greater than or equal to" or "less than or equal to" are also standard comparison operators available in basic filter logic.

1. Workday, Inc. (2023). Prism Analytics: Prepare Data.

Section: Filter Stage. This document details the functionality of the Filter stage in data preparation. It explicitly states, "By default, Workday connects all filter conditions with an AND operator. To use OR logic or a combination of AND and OR logic, you must enable Advanced Logic." This directly confirms that combining AND/OR operators is the specific use case for advanced logic. The document also shows that basic filtering includes options for handling NULLs and using standard comparison operators like "equal to," "not equal to," "greater than," and "less than."