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Workday Pro Prism Analytics Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
What report can you run to edit and maintain your Prism import and publish schedules?

A. Prism Management Console
B. Prism Activities Monitor
C. Prism Usage
D. Scheduled Future Processes

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, managing schedules for importing data into tables or publishing datasets as Prism data sources is a key administrative task. According to the official Workday Prism Analytics study path documents, the Scheduled Future Processes report (option A) is the tool used to edit and maintain Prism import and publish schedules. This report provides a centralized view of all scheduled processes in Workday, including Prism-related tasks such as Data Change tasks (for imports) and dataset publish schedules. Users can access this report to view, edit, or cancel scheduled processes, ensuring that data imports and publishes occur at the desired frequency and time.
The other options are incorrect:
* B. Prism Management Console: The Prism Management Console provides an overview of Prism activities and resources but does not allow for editing or maintaining schedules.
* C. Prism Activities Monitor: This report monitors the status of Prism activities (e.g., running or completed tasks) but does not manage schedules.
* D. Prism Usage: The Prism Usage report tracks usage metrics for Prism Analytics but does not handle scheduling tasks.
The Scheduled Future Processes report is the correct tool for managing Prism import and publish schedules, ensuring efficient data updates.
References:
Workday Prism Analytics Study Path Documents, Section: Publishing and Visualizing Data, Topic: Managing Import and Publish Schedules Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Using Scheduled Future Processes for Prism Tasks

NEW QUESTION # 24
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 marked as required.
B. The custom report prompts have default values assigned on the report definition.
C. The report is built on an indexed data source.
D. The prompts are mapped at the data change task level.

Answer: B

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 # 25
When joining datasets, what items must match?

A. The field types for the Match Row fields.
B. The number of rows in each dataset.
C. The level of detail in each dataset.
D. The field names for the Match Row fields.

Answer: A

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 # 26
For a Prism use case, you have two datasets: one contains daily sales data, and the other contains monthly budget allocations. Before performing a join between these datasets, what transformation stage should you apply to the sales data to ensure it matches the granularity of the budget data?

A. Group By
B. Manage Fields
C. Union
D. Filter

Answer: A

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 # 27
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 # 28
......

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