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Universal Containers (UC) has recently received an increased number of support cases. As a result, UC has hired more customer support reps and has started to assign some of the ongoing cases to newer reps.
Which generative AI solution should the new support reps use to understand the details of a case without reading through each case comment?
New customer support reps at Universal Containers can use Einstein Work Summaries to quickly understand the details of a case without reading through each case comment. Work Summaries leverage generative AI to provide a concise overview of ongoing cases, summarizing all relevant information in an easily digestible format.
Einstein Copilot can assist with a variety of tasks but is not specifically designed for summarizing case details.
Einstein Sales Summaries are focused on summarizing sales-related activities, which is not applicable for support cases.
For more details, refer to Salesforce documentation on Einstein Work Summaries.
Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page.
After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?
For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.
Option A (Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.
Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus onAction Instructions. Here's why:
Action Instructionsdefine the logic, parameters, and steps the AI should follow to execute the action. They include:
How input data is processed.
API calls or Apex invocations.
Conditional logic (e.g., decision trees). Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures.
Action Input(Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctlyusethe inputs.
Action Name(Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
By iterating onAction Instructions, the AI Specialist ensures the action's logic, integrations, and error handling are robust.
An AI Specialist wants to use the related lists from an account in a custom prompt template.
What should the AI Specialist consider when configuring the prompt template?
When configuring a custom prompt template to use related lists, the AI Specialist must be aware of the maximum number of related list merge fields that can be included. Salesforce enforces limits to ensure prompt templates perform efficiently and do not overload the system with too much data. As a best practice, it's important to monitor and optimize the number of merge fields used.
Option B is correct because there is a limit on how many related list merge fields can be included in a prompt template.
Option A (text encoding) and Option C (XML/JSON rendering) are not key considerations in this context.
An AI Specialist has created a copilot custom action using flow as the reference action type. However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting.
What should the AI Specialist do to identify the root cause of the problem?
When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder's Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.
Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with dynamic debugging.
Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.
