Blog

Frank Moore Frank Moore

0 Course Enrolled • 0 Course Completed

Biography

Valid Test Workday Workday-Prism-Analytics Experience & Exam Sample Workday-Prism-Analytics Online

We never give up the sustainable development, so we revamp our Workday-Prism-Analytics practice materials' versions constantly. Nowadays, the market softens because of oversupply, but the demand of our Workday-Prism-Analytics learning braindumps are increasing all the time. It is lucky our Workday-Prism-Analytics Guide prep offers tremendous knowledge for you, so look forward to cooperate fervently. And the service will last for a year long after your purchase for we provide free updates for one year long!

Normally, you will come across almost all of the real test questions on your usual practice. Maybe you are doubtful about our Workday-Prism-Analytics training questions. We have statistics to tell you the truth. The passing rate of our products is the highest according to the investigation. Many candidates can also certify for our Workday-Prism-Analytics Study Materials. As long as you are willing to trust our Workday-Prism-Analytics preparation materials, you are bound to get the certificate.

>> Valid Test Workday Workday-Prism-Analytics Experience <<

Workday certification Workday-Prism-Analytics the latest exam questions and answers

The web-based Workday Workday-Prism-Analytics mock test is compatible with mamy systems. This version of the Workday Workday-Prism-Analytics practice exam requires an active internet connection. It does not require any additional plugins or software installation to operate. Furthermore, others also support the Workday-Prism-Analytics web-based practice exam. Features of the Workday-Prism-Analytics desktop practice exam software are web-based as well.

Workday Pro Prism Analytics Exam Sample Questions (Q26-Q31):

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. Filter
C. Manage Fields
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 # 27
A Prism data writer needs to create a new Prism calculated field on a derived dataset using the CASE function. When creating a calculated field, what symbol do you use to view a list of fields that you can select from in the dataset?

A. (
B. #
C. {
D. [

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, when creating a calculated field in a derived dataset, users often need to reference existing fields in the dataset within their expressions, such as in a CASE function. According to the official Workday Prism Analytics study path documents, to view and select from a list of available fields in the dataset while building a calculated field expression, the user types the [ symbol (left square bracket). This symbol triggers a dropdown list of all fields in the dataset, allowing the user to select the desired field without manually typing its name, reducing the risk of errors. For example, typing [ and selecting a field like
"Employee_ID" will insert [Employee_ID] into the expression, which can then be used in the CASE function logic.
The other symbols do not serve this purpose:
* B. (: Parentheses are used for grouping expressions or defining function parameters, not for field selection.
* C. #: The hash symbol is not used in Prism Analytics for field selection; it may be associated with other functionalities in different contexts.
* D. {: Curly braces are not used for field selection in Prism Analytics; they may be used in other systems for different purposes, such as templating.
The use of the [ symbol ensures an efficient and accurate way to reference fields in a calculated field expression, streamlining the creation process in Prism Analytics.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Creating Calculated Fields in Derived Datasets Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Using the Expression Editor for Calculated Fields

NEW QUESTION # 28
You are loading data into a table using the Data Change task. The field type in the source file is Numeric and the table field type is Text. What can you do to load the data?

A. Map the Numeric field to the table Text field.
B. Change the connection type for the data change task.
C. Change the field type in the parsing stage from Numeric to Text.
D. Use a different source file, as the field types are incompatible.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Workday Prism Analytics, the Data Change task is used to load data from a source file into a table, and it supports flexible field type mapping to accommodate differences between source and target field types.
According to the official Workday Prism Analytics study path documents, when the source file has a Numeric field type and the target table has a Text field type, you can map the Numeric field to the Text field in the Data Change task. Workday Prism Analytics automatically handles the conversion of Numeric values to Text during the data loading process, as Text fields can store Numeric values as strings without data loss.
The other options are not necessary or appropriate:
* A. Use a different source file, as the field types are incompatible: The field types are not incompatible; Prism supports mapping Numeric to Text, making a new source file unnecessary.
* C. Change the field type in the parsing stage from Numeric to Text: The parsing stage defines how the source file is interpreted, but changing the source field type is not required since the mapping handles the conversion.
* D. Change the connection type for the data change task: The connection type (e.g., file upload or Workday report) is unrelated to field type compatibility and does not address the issue.
By mapping the Numeric field to the Text field in the Data Change task, the data can be successfully loaded, leveraging Prism's built-in type conversion capabilities to ensure compatibility.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Field Type Mapping in Data Change Tasks Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Handling Field Type Conversions in Data Loading

NEW QUESTION # 29
You have two tables. One with employee data from Workday and another with learner data from an external system. Both tables have an Employee ID field.
In the Employee Data TBL, Employee ID is text.

In the Learner Data TBL, Employee ID is numeric.

How can you prepare to join these tables, without the potential loss of data?

A. Import the Learner Data TBL into a DDS and change the field type of Employee ID from numeric to text using a Manage Fields stage.
B. Change the field type of Employee ID directly on the Employee Data TBL from text to numeric.
C. 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.
D. Change the field type of Employee ID directly on the Learner Data TBL from Numeric to Text.

Answer: A

Explanation:
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.
References:
Workday Prism Analytics Study Path Documents, Section: Data Prep and Transformation, Topic: Preparing Data for Joins in Prism Analytics Workday Prism Analytics Training Guide, Module: Data Prep and Transformation, Subtopic: Field Type Transformations Using Manage Fields Stage

NEW QUESTION # 30
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. Create a derived dataset and build a CASE calculated field to classify workers against their pay.
C. Build a CASE calculated field function on the TBL directly to ease later transformation.
D. Add the additional field to your raw data before you ingest into Prism.

Answer: B

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 # 31
......

Our Workday-Prism-Analytics practice exam simulator mirrors the Workday-Prism-Analytics exam experience, so you know what to anticipate on Workday-Prism-Analytics exam day. Our Workday Workday-Prism-Analytics features various question styles and levels, so you can customize your Workday-Prism-Analytics exam questions preparation to meet your needs.

Exam Sample Workday-Prism-Analytics Online: https://www.testkingpass.com/Workday-Prism-Analytics-testking-dumps.html

Now, make a risk-free investment in training and certification with the help of Workday-Prism-Analytics practice torrent, Workday Valid Test Workday-Prism-Analytics Experience Our customer support is available for you 24/7, Workday Valid Test Workday-Prism-Analytics Experience Based on your situation, including the available time, your current level of knowledge, our study materials will develop appropriate plans and learning materials, To help you pass Workday-Prism-Analytics exam test is recognition of our best efforts.

Although this is a legacy Microsoft protocol, it is still required in many Workday-Prism-Analytics Questions environments, and Microsoft wants to be absolutely sure you understand how it works, Ideas are always floating out there in the nether regions.

Latest Valid Test Workday-Prism-Analytics Experience & Free Demo Exam Sample Workday-Prism-Analytics Online: Workday Pro Prism Analytics Exam

Now, make a risk-free investment in training and certification with the help of Workday-Prism-Analytics practice torrent, Our customer support is available for you 24/7, Based on your situation, including the available time, your Workday-Prism-Analytics current level of knowledge, our study materials will develop appropriate plans and learning materials.

To help you pass Workday-Prism-Analytics exam test is recognition of our best efforts, It is easy to get bogged down by all the material you need to learn and lose sight of your goal.

Cookie	Duration	Description
cookielawinfo-checkbox-analytics	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional	11 months	The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance	11 months	This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy	11 months	The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Frank Moore Frank Moore

Biography

COOKIE NOTICE