WORKDAY-PRISM-ANALYTICS REAL EXAMS & WORKDAY-PRISM-ANALYTICS EXAM BOOK

Workday-Prism-Analytics Real Exams & Workday-Prism-Analytics Exam Book

Workday-Prism-Analytics Real Exams & Workday-Prism-Analytics Exam Book

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Useful Workday-Prism-Analytics Real Exams & Leader in Qualification Exams & Practical Workday Workday Pro Prism Analytics Exam

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Workday Pro Prism Analytics Exam Sample Questions (Q51-Q56):

NEW QUESTION # 51
You are a new Prism customer and you want to ensure the correct set of fields is brought into a derived dataset. When should you apply a Manage Fields stage?

  • A. At the beginning of the primary pipeline of the Base Dataset.
  • B. After the dataset is published.
  • C. At the beginning of the Primary Pipeline of the derived dataset.
  • D. At the end of the Primary Pipeline of a published dataset.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, a Manage Fields stage is used to control the fields in a dataset by renaming, hiding, or changing field types, among other actions. According to the official Workday Prism Analytics study path documents, to ensure the correct set of fields is brought into a derived dataset (DDS), the Manage Fields stage should be applied at the beginning of the Primary Pipeline of the derived dataset (option C).
Placing the Manage Fields stage early in the pipeline-right after the initial import stage (Stage 1)-allows you to define the field structure upfront, ensuring that subsequent transformationstages (e.g., Join, Filter, Calculate Field) operate on the desired set of fields. This approach helps maintain consistency and avoids unnecessary processing of fields that are not needed in later stages.
The other options are not optimal:
* A. After the dataset is published: You cannot add transformation stages like Manage Fields after a dataset is published; transformations must be applied during the dataset's creation or editing.
* B. At the end of the Primary Pipeline of a published dataset: Similar to option A, you cannot modify a published dataset's pipeline, and placing Manage Fields at the end would not prevent unnecessary fields from being processed in earlier stages.
* D. At the beginning of the primary pipeline of the Base Dataset: A Base Dataset does not have a transformation pipeline; it is a direct import of a table, so Manage Fields stages can only be applied in a Derived Dataset.
Applying the Manage Fields stage at the beginning of the derived dataset's Primary Pipeline ensures efficient data preparation and transformation.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Using Manage Fields in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Best Practices for Field Management in Pipelines


NEW QUESTION # 52
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. Filter
  • D. Union

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 # 53
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. Build an Evaluate Expression calculated field on your final Prism business object to evaluate workers against their pay.
  • B. Build a CASE calculated field function on the TBL directly to ease later transformation.
  • C. Add the additional field to your raw data before you ingest into Prism.
  • D. Create a derived dataset and build a CASE calculated field to classify workers against their pay.

Answer: D

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 # 54
When using a window function to calculate averages in Prism, what field type must the function operate on?

  • A. Numeric
  • B. Date
  • C. Text
  • D. Boolean

Answer: A

Explanation:
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.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Window Functions and Field Type Requirements Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using AVG in Window Functions


NEW QUESTION # 55
Why should you include Workday instance field types in the Workday report that you use to import data into Prism?

  • A. The final Prism datasource can support drilling into Workday objects.
  • B. Performance is improved in the final Prism datasource when published.
  • C. Joins are more easily performed with instance field types.
  • D. Unions are more easily performed with instance field types.

Answer: A

Explanation:
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.
References:
Workday Prism Analytics Study Path Documents, Section: Integrating Prism with Workday Reports, Topic:
Workday Report Field Types and Prism Integration
Workday Prism Analytics Training Guide, Module: Publishing and Visualizing Data, Subtopic: Enabling Drill-Down Capabilities in Prism Data Sources


NEW QUESTION # 56
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