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Step-by-Step Explanation

Purpose of the Query:

The task is to retrievefield optionsfrom acustom form field. This operation involves fetching data, not creating or modifying it, which requires a GET method.

Correct API Structure:

The URL must follow Workfront's API structure:

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https://{your-workfront-domain}/attask/api/{version}/{endpoint}

OptionBfollows this standard structure, where:

wfdomain.workfront.com is the placeholder for the Workfront instance.

/attask/api/v12.0 specifies the API version.

/PARAM/search is the endpoint to search for parameters or fields.

Why Not Other Options?

A: The use of POST is incorrect because it is meant for creating or updating resources, not retrieving data. Additionally, the URL structure is incorrect and includes unnecessary query strings (username, password) not relevant for this operation.

C: While the method GET is correct, the URL (PARAM/search) is incomplete and lacks the required Workfront API structure, making it invalid.

References

Workfront API Documentation: Querying and Retrieving Custom Form Field Data

Experience League Community: Best Practices for Using GET Methods in Workfront API=========================

Step by Step Comprehensive Detailed Explanation:

Problem Summary:

The organization requires a process to track every project update made by Fusion to meet compliance standards.

This process must provide a clear audit trail of updates, including details like user and timestamp.

Option Analysis:

A. Use reporting on the Last Updated by ID and Last Update Date:

While this provides basic reporting, it only reflects the most recent update and does not maintain a comprehensive history of changes over time.

B. Update the External Reference ID with User ID and Timestamp:

Updating the External Reference ID could cause issues if this field is used for other purposes. It is not designed for logging multiple updates.

C. Create a Note record related to the record updated:

Correct. Creating a Note record for each update ensures that every change is logged with relevant details (e.g., user, timestamp, update reason). This approach creates a full audit trail that is easily accessible and reportable.

Why Note Records are Best:

Audit Trail: Notes provide a clear and searchable history of updates for each project.

Compliance: Ensures compliance by documenting who made what changes and when.

Flexibility: Notes can include custom details such as update reasons or additional context, making them more robust than standard fields.

Implementation:

In the Fusion scenario, add a module to create a Note record after each update.

Populate the Note with relevant details, such as:

User ID ({lastUpdatedBy})

Timestamp ({lastUpdateDate})

Description of the change.

Understanding Instant and Polling Triggers:

Instant Triggers:

Rely on webhooks to receive real-time data from a third-party system.

The external system sends a notification (webhook) to Fusion whenever an event occurs, triggering the scenario immediately.

Polling Triggers:

Regularly check (poll) the third-party system for new or updated records at scheduled intervals.

These are slower because they involve repeated API requests.

Why Option B is Correct:

Speed and Efficiency:

Instant triggers process data faster because they act immediately upon receiving a webhook. Polling triggers, on the other hand, may take time depending on the polling frequency and can result in unnecessary delays.

Reduced Load on Systems:

Instant triggers generate fewer API calls than polling triggers, which continuously check for new records even if no changes have occurred.

Best Practice: Use instant triggers whenever supported by the third-party system to ensure faster and more efficient scenario execution.

Why the Other Options are Incorrect:

Option A ("Instant triggers store received webhooks in a queue"):

Webhooks do not store data in a queue; they simply notify Fusion of events in real-time. Polling triggers also do not store records but remember the last processed record.

Option C ("A user must set up a webhook in Fusion"):

Instant triggers require setting up webhooks in the external system, not in Fusion. Fusion provides the webhook endpoint, but the user must configure the source system to send data.

Option D ("Only polling triggers can be set to run on a schedule"):

This is incorrect because instant triggers do not rely on schedules; they operate in real-time. Polling triggers, however, run on schedules and are used when instant triggers are unavailable.

References and Supporting Documentation:

Adobe Workfront Fusion Triggers Documentation

Workfront Community: Differences Between Instant and Polling Triggers

Instant triggers are the preferred option when available, as they provide real-time data processing with greater speed and efficiency than polling triggers.

Understanding the Requirement:

The user wants to upload a document into Workfront and attach it to a specific task.

This action involves creating a document in Workfront and associating it with a task as a related record.

Why Option B is Correct:

TheUpload Documentmodule is specifically designed for uploading files into Workfront.

It includes the ability to set arelated record(e.g., a task, project, or issue) to which the document will be attached.

This ensures the document is uploaded and correctly linked to the task in a single operation.

Why the Other Options are Incorrect:

Option A ("Create Record for Document Version after Create Record for the document on the task"):

This involves multiple steps, which are unnecessary. TheUpload Documentmodule already handles both the upload and the attachment in one action.

Option C ("Create Record of Document type while setting the related record"):

TheCreate Recordmodule is not designed for file uploads. It only creates metadata records, not the actual document.

Option D ("Miscellaneous Action to attach document to a task"):

There is noMiscellaneous Actionspecifically for attaching a document to a task. TheUpload Documentmodule is the appropriate choice.

Steps to Upload a Document in Workfront Fusion:

Add theUpload Documentmodule to the scenario.

Specify the file to upload (e.g., from a previous module like Google Drive or an HTTP request).

Set therelated recordto the target task by providing its ID.

Run the scenario to upload and attach the document to the task.

References and Supporting Documentation:

Adobe Workfront Fusion: Upload Document Module

Workfront Community: Best Practices for Document Management in Fusion

The Upload Document module is the most efficient and accurate method for uploading and attaching a document to a task in Workfront.

Step 1: Query Workfront for User Based on Email Address

Salesforce provides the email address of the user. Since Workfront uses user IDs for task assignments, the email address can be used as a unique identifier to query Workfront’s database.

A query module in Workfront Fusion retrieves the corresponding Workfront user ID using the provided email address.

Step 2: Assign Task Using Returned ID

After retrieving the Workfront user ID, use it in the assignment field of the task module in Fusion.

The task assignment process requires a valid user ID, ensuring proper linkage and assignment within Workfront.

Why Not Other Options?

A. Assign the task using the provided user ID > add the user ID to the assignment field: The Salesforce user ID is not recognized by Workfront. It is necessary to query Workfront to convert the email into a Workfront-compatible user ID.

C. Query Workfront based on the user's name > assign task using returned email address: Workfront uses user IDs, not email addresses, for task assignments. Names are also not unique and could cause assignment errors.

References:

Adobe Workfront Fusion Documentation: Task Assignments Using User IDs

Experience League Community: Mapping Salesforce Data to Workfront Tasks

Step by Step Comprehensive Detailed Explanation:

Understanding the Scenario:

The diagram shows two routes, each leading to an aggregator module (Aggregator 1 and Aggregator 2).

The bundle inspector indicates different output counts for the two aggregators: Aggregator 1 displays 1, and Aggregator 2 displays 10.

Option Analysis:

A. Aggregator 1’s route is set to process the 1st bundle only, and Aggregator 2's route is set to process all bundles:

Incorrect. Aggregators process data from their input modules based on their configuration. While filters or limits might be applied earlier in the flow, the scenario does not suggest that the aggregator's configuration explicitly limits bundles in this way.

B. The source module for the aggregators are different:

Correct. The two aggregators receive input from different modules. For example, Aggregator 1 might aggregate bundles from "Projects," which outputs a single bundle, while Aggregator 2 aggregates bundles from "Issues," which outputs 10 bundles. This explains the differing numbers in the bundle inspector.

C. Aggregator 2 displays a 10 in the bundle inspector because it is set to repeat 10 times:

Incorrect. Aggregators do not "repeat" a specific number of times. Instead, they process the input bundles passed to them and output a result based on the aggregation logic.

D. The scenario's router is set to only allow projects through the route to Aggregator 1 and only allows tasks through the route to Aggregator 2:

Incorrect. While the router might direct specific bundles (e.g., "Projects" to Aggregator 1 and "Issues" to Aggregator 2), the difference in bundle counts is determined by the source modules, not by the router's configuration alone.

Why Source Modules Determine Bundle Counts:

Each aggregator processes data from a source module. If the source module outputs different numbers of bundles, the aggregators will show different bundle counts.

In this example, "Projects" might output only 1 bundle (e.g., 1 project), while "Issues" outputs 10 bundles (e.g., 10 issues).

How to Verify:

Inspect the source modules feeding into each aggregator. Check the number of bundles they produce during the scenario run.

Confirm that the aggregators are aggregating based on their respective inputs.

References:This explanation aligns with Workfront Fusion's handling of bundles and aggregation logic. Differences in bundle counts at the aggregator level are typically due to differences in the source module outputs, as shown in the scenario.

Understanding the Scenario:

The image represents a Workfront Fusion scenario with a Workfront module, HTTP modules, and routers splitting the execution path.

The goal is to reuse multiple fields from the Workfront module (e.g., data extracted or processed earlier in the flow) as mapped expressions in HTTP PUT requests located in the bottom paths.

Why Option A is Correct:

Set Multiple Variables Module: This module allows you to define and store multiple variables at a single point in the scenario (e.g., after the Workfront module). These variables can then be reused throughout subsequent steps in the scenario.

Get Multiple Variables Module: By placing this module between the Text Parser and the bottom Router, you can retrieve all previously stored variables, ensuring consistent access across all branches of the flow. This avoids redundancy and ensures the data is easily accessible for each HTTP request in the bottom paths.

Why the Other Options are Incorrect:

Option B ("Set Multiple Variables after Workfront, Get Variables before each HTTP module"): This is partially correct but less efficient. Adding multiple Get Variable modules before each HTTP request results in repetitive configuration and increases maintenance complexity.

Option C ("Set Variable and Get Variable for each HTTP module"): Using individual Set and Get Variable modules increases duplication. This approach requires separate variables for each data point, which is inefficient compared to using the Set/Get Multiple Variables module for multiple fields at once.

Steps to Configure the Solution:

After the Workfront module:

Add aSet Multiple Variablesmodule.

Define all the fields required for the HTTP PUT requests as variables, mapping them from the Workfront module outputs.

Between the Text Parser and the bottom Router:

Add aGet Multiple Variablesmodule.

Retrieve the previously stored variables, ensuring they are accessible for all paths.

In the HTTP modules on each bottom path:

Use the retrieved variables for mapping in the PUT requests.

How This Solves the Problem:

This approach centralizes variable management, making it easier to access and modify data as needed.

It avoids redundancy, as variables are defined once and reused across all paths, reducing the risk of errors and ensuring consistency.

References and Supporting Documentation:

Adobe Workfront Fusion: Variables Module Overview

Workfront Community: Efficient Use of Variables in Fusion

Step by Step Comprehensive Detailed Explanation:

Understanding the Problem:

Multiple JSON packets with nested arrays are being returned by queries.

The goal is to process each object within these JSON arrays through the scenario.

Option Analysis:

A. Define the data structure > then run the Iterator to Parse each JSON packet:

Incorrect. While defining a data structure is necessary, running the Iterator first would fail to process the JSON properly if it is not parsed.

B. Concatenate the JSON > Define the data structures > Parse the JSON > then run the Iterator:

Incorrect. Concatenation is unnecessary for this scenario since each JSON packet can be parsed and processed independently.

C. Define the data structure > Parse the JSON > then process arrays in the Iterator:

Correct. The correct approach involves defining a data structure to map the JSON, parsing it to extract the data into usable fields, and then using an Iterator module to process each object in the nested arrays.

D. Merge the JSON > Parse the JSON > then use the Iterator:

Incorrect. Merging JSON packets is not required unless you explicitly need to combine data from multiple packets into a single structure, which is not mentioned in this scenario.

Why This Workflow Works:

Defining the Data Structure: Helps Fusion understand and map the JSON fields for processing.

Parsing the JSON: Extracts the data into fields and arrays that can be further processed.

Using the Iterator: Breaks down the nested arrays into individual objects for sequential processing through the scenario.

Implementation Steps:

Use aDefine Data Structuremodule to define the JSON schema (fields, arrays, and objects).

Add aParse JSONmodule to convert raw JSON packets into mapped data fields.

Add anIteratormodule to process individual objects in the nested arrays.

Understanding the Requirement:

The project name in Workfront must include:

Purchase Order Number (PO#).

Name of the product.

PO Fulfillment Date (formatted as YY/MM/DD).

If a discount is provided, append the discount percentage and the approver's last name in the format: 15% Discount - UserLast.

Example Output:

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2837 - Compendium-Premium Running Shoes - 21/02/16 - 15% Discount - UserLast

Why Option A is Correct:

The expression in Option A achieves the desired result step-by-step:

3.PO#: Includes the PO number.

3.Name: Appends the name of the product.

formatDate(3.PO Fulfillment Date, YY/MM/DD): Formats the PO Fulfillment Date in the desired YY/MM/DD format.

if(3.Discount Provided? = "Yes"; "- " + 3.Discount % + " Discount - " + 3.Approver Last Name):

Uses the if function to check if a discount was provided.

If "Yes," it appends the discount percentage (3.Discount %) and approver's last name (3.Approver Last Name) with the required format.

Why the Other Options are Incorrect:

Option B:

The expression is incorrect because it places if(3.Discount Provided?...) directly after PO Fulfillment Date without using formatDate for date formatting. This results in an unformatted date.

Option C:

Uses parseDate instead of formatDate, which is incorrect in this context. parseDate is used to interpret strings as dates, not to format dates for output.

Option D:

The expression does not include any conditional logic for checking if a discount is provided. It simply appends Discount - Approver Last Name regardless of whether a discount was given.

Steps to Use the Expression in Workfront Fusion:

Place the expression in the module that defines the project title in Workfront.

Map the fields (PO#, Name, PO Fulfillment Date, etc.) to the respective columns from the CSV data.

Test the scenario to ensure the output matches the desired format.

Final Expression (Option A):

3.PO# + " - " + 3.Name + " - " + formatDate(3.PO Fulfillment Date; YY/MM/DD) + if(3.Discount Provided? = "Yes"; " - " + 3.Discount % + " Discount - " + 3.Approver Last Name)

References and Supporting Documentation:

Adobe Workfront Fusion Functions Documentation

Workfront Community: Handling Conditional Logic and Date Formatting

Understanding the Requirement:

The input is an array containing objects, and the goal is to extract all the stat.name values into acomma-separated string.

Example Input:

[

{

"base_stat": 48,

"effort": 1,

"stat": {

"name": "hp",

"url": "https://pokeapi.co/api/v2/stat/1/"

}

},

{

"base_stat": 48,

"effort": 0,

"stat": {

"name": "attack",

"url": "https://pokeapi.co/api/v2/stat/2/"

}

}

]

Example Output:"hp, attack"

Why Option B is Correct:

The expressionjoin(map(2.data: stats[]; stats.stat.name); ", "):

map: Iterates through each object in the array (2.data: stats[]) and extracts the stat.name field.

join: Combines the extracted values into a single string, separated by a comma and space (", ").

Breaking it down:

map(2.data: stats[]; stats.stat.name) → Creates an array of names: ["hp", "attack"].

join(...; ", ") → Converts the array into the string "hp, attack".

Why the Other Options are Incorrect:

Option A: join(2.data: stats[]; stat.name; ", ")

This syntax is incorrect because it attempts to directly access stat.name within the join function without first mapping the values.

Option C: join(map(2.data: stats[]; stat.name); ", ")

The mapping references stat.name directly but does not account for the nested structure (stats.stat.name).

Option D: join(flatten(2.data: stats[]); ", ")

The flatten function is unnecessary here as the data is already structured. It would not properly extract the stat.name values.

Steps to Implement in Workfront Fusion:

Add aMapping/Transformation Module.

Use the join(map(...)) function as described to transform the input array into a comma-separated string.

Test the output to ensure it correctly generates the desired format.

How This Solves the Problem:

The map function ensures the proper extraction of nested stat.name values.

The join function combines these values into the desired format efficiently.

References and Supporting Documentation:

Adobe Workfront Fusion Functions Documentation

Workfront Community: Using Map and Join Functions

The combination of map and join ensures that the stat names are extracted and formatted into a single comma-separated string, as required.