Universal Containers (UC) has configured an Agentforce Data Library using Knowledge articles. When testing in Agent Builder and the Experience Cloud site, the agent is not responding with grounded Knowledge article information. However, when tested in Prompt Builder, the response returns correctly. What should UC do to troubleshoot the issue?

Correct Answer:C
Comprehensive and Detailed In-Depth Explanation:UC has set up an Agentforce Data Library with Knowledge articles, and while Prompt Builder retrieves the data correctly, the agent fails to do so in Agent Builder and Experience Cloud. Let??s troubleshoot the issue.
✑ Option A: Create a new permission set that assigns "Manage Knowledge" and assign it to the Agentforce Service Agent User.The "Manage Knowledge" permission is for authoring and managing Knowledge articles, not for reading or retrieving them in an agent context. The Agentforce Service Agent User (a system user) needs read access to Knowledge, not management rights. This option is excessive and irrelevant to the grounding issue, making it incorrect.
✑ Option B: Ensure the assigned User permission set includes access to the prompt template used to access the Knowledge articles.Prompt templates in Prompt Builder don??t require specific permissions beyond general Einstein Generative AI
access. Since the Prompt Builder test works, the template and its grounding are accessible to the testing user. The issue lies with the agent??s runtime access, not the template itself, making this incorrect.
✑ Option C: Ensure the Data Cloud User permission set has been assigned to the Agentforce Service Agent User.When Knowledge articles are grounded via an Agentforce Data Library, they are often ingested into Data Cloud for indexing and retrieval. The Agentforce Service Agent User, which runs the agent, needs the "Data Cloud User" permission set (or equivalent) to access Data Cloud resources, including the Data Library. If this permission is missing, the agent cannot retrieve Knowledge article data during runtime (e.g., in Agent Builder or Experience Cloud), even though Prompt Builder (running under a different user context) succeeds. This is a common setup oversight and aligns with the symptoms, making it the correct answer.
Why Option C is Correct:The Agentforce Service Agent User??s lack of Data Cloud access explains the failure in agent-driven contexts while Prompt Builder (likely run by an admin with broader permissions) succeeds. Assigning the "Data Cloud User" permission set resolves this, per Salesforce documentation.
References:
✑ Salesforce Agentforce Documentation: Data Library Setup > Permissions – Requires Data Cloud access for agents.
✑ Trailhead: Ground Your Agentforce Prompts – Notes Data Cloud User permission for Knowledge grounding.
✑ Salesforce Help: Agentforce Security > Agent User Setup – Lists required permission sets.

Universal Containers (UC) wants to create a new Sales Email prompt template in Prompt Builder using the "Save As" function. However, UC notices that the new template produces different results compared to the standard Sales Email prompt due to missing hyperparameters.
What should UC do to ensure the new prompt template produces results comparable to the standard Sales Email prompts?

Correct Answer:B
When Universal Containers creates a new Sales Email prompt template using the "Save As" function, missing hyperparameters can result in different outputs. To ensure the new prompt produces comparable results to the standard Sales Email prompt, the Agentforce Specialist should manually add the necessary hyperparameters to the new template.
✑ Hyperparameters like Temperature, Frequency Penalty, and Presence Penalty
directly affect how the AI generates responses. Ensuring that these are consistent with the standard template will result in similar outputs.
✑ Option A (Model Playground) is not necessary here, as it focuses on fine-tuning
models, not adjusting templates directly.
✑ Option C (Reverting to the standard template) does not solve the issue of customizing the prompt template.
For more information, refer to Prompt Builder documentation on configuring hyperparameters in custom templates.

After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?

Correct Answer:C
The Temperature hyperparameter controls the randomness of model outputs:
✑ Low Temperature (e.g., 0.2): More deterministic, consistent responses.
✑ High Temperature (e.g., 1.0): More creative, varied responses.
✑ Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
✑ Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
✑ Einstein Studio Documentation: Model Hyperparameters
✑ Explicitly states "Temperature adjusts the balance between predictable and random outputs."