AD0-E902

Explanation:
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 a Define Data Structure module to define the JSON schema (fields, arrays, and objects).
Add a Parse JSON module to convert raw JSON packets into mapped data fields.
Add an Iterator module to process individual objects in the nested arrays.

Explanation:
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:
The Upload Document module is specifically designed for uploading files into Workfront.
It includes the ability to set a related 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. The Upload Document module already handles both the upload and the attachment in one action.
Option C ("Create Record of Document type while setting the related record"):
The Create Record module 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 no Miscellaneous Action specifically for attaching a document to a task. The Upload Document module is the appropriate choice.
Steps to Upload a Document in Workfront Fusion:
Add the Upload Document module to the scenario.
Specify the file to upload (e.g., from a previous module like Google Drive or an HTTP request).
Set the related record to the target task by providing its ID.
Run the scenario to upload and attach the document to the task.
Reference 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.

Explanation:
Understanding the Requirement:
The input is an array containing objects, and the goal is to extract all the stat.name values into a comma-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 expression join(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 a Mapping/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. Reference 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.

Explanation:
Understanding the Issue:
The user observes that all task due dates are incorrect due to a mismatch in the time zone.
The solution must ensure that the correct time zone is applied universally across all scenarios and
dates within the organization's Fusion instance.
Why Option B is Correct:
Fusion Organization's Time Zone Setting:
Changing the time zone at the organization level ensures that all scenarios within the organization adopt the updated time zone setting.
This change applies globally, making it the simplest and most efficient method to ensure consistency across all dates and scenarios.
This adjustment prevents the need for scenario-specific or localized changes, saving time and reducing errors.
Why the Other Options are Incorrect:
Option A ("Set a variable for every date using formatDate"):
While the formatDate function can adjust time zones for individual dates, applying this approach to every date in every scenario is highly inefficient and error-prone. It does not offer a global solution. Option C ("Change the scenario's time zone default"):
Scenarios in Fusion do not have a specific "time zone default" setting. The organization’s time zone setting is the controlling factor.
Option D ("Change the time zone in the computer's localization settings"):
Fusion scenarios run on cloud servers, not the user’s local machine. Changing the computer’s time zone would have no effect on the scenarios’ behavior. Steps to Change the Organization’s Time Zone:
Log in to Adobe Workfront Fusion.
Navigate to the Organization Settings:
Go to the Admin Panel or the organization settings section.
Locate the Time Zone setting.
Select the desired time zone from the dropdown list.
Save the changes.
All scenarios will now adopt the updated time zone setting.
How This Solves the Problem:
Changing the organization's time zone applies a consistent time zone across all dates used in scenarios. This ensures accuracy without requiring individual scenario adjustments or manual interventions.
Reference and Supporting Documentation:
Adobe Workfront Fusion Official Documentation: Organization Settings
Workfront Community: Best Practices for Time Zone Configuration

Explanation:
Step by Step Comprehensive Detailed Explanation
Understanding the Requirement:
The user needs to archive the last five versions of a scenario for one year.
Archiving ensures there is a record of previous versions in case rollback or review is needed.
Option Analysis:
A. Save the scenario frequently:
Incorrect. While frequent saving ensures changes are not lost, it does not provide an archival mechanism for version history.
B. Download the scenario blueprints:
Correct. Downloading blueprints of the scenario allows the user to store version snapshots externally. Blueprints include the complete design and settings of the scenario, making them ideal for archival purposes.
C. Clone the scenario anytime the design changes:
Incorrect. Cloning creates duplicates of the scenario but does not inherently manage or track version
history for archival purposes.
D. Find previous versions using the History tab:
Incorrect. The History tab only shows recent edits and logs but does not provide a long-term archiving solution.
Why Downloading Blueprints is Best:
External Storage: Blueprints can be downloaded and stored securely for long-term use.
Restoration: A saved blueprint can be re-imported into Fusion to restore a scenario exactly as it was. Version Control: Allows the user to manually manage and organize multiple scenario versions over time.
Implementation Steps:
Go to the scenario in Workfront Fusion.
Use the Download Blueprint option to save a copy of the scenario.
Label and organize the blueprints by version and date for easy retrieval later.