Salesforce Updated Salesforce-AI-Specialist Exam Questions and Answers by mahnoor

After modifying masked fields in Einstein's Trust Layer, the next important step is to test and confirm that the responses generated by prompts utilizing the newly masked data still meet quality standards. This ensures that masking sensitive information does not negatively impact the usefulness or accuracy of the AI-generated content. Thorough testing helps identify any issues in prompt performance that could arise due to masking, and adjustments can be made if needed.

Option B is correct because testing the effects of masking on AI responses is a critical step in ensuring AI continues to function as expected.

Option A (turning off and on the Einstein Trust Layer) is unnecessary after changing the masked fields.

Option C (turning on Einstein Feedback) allows for user feedback but is not a direct step following field masking modifications.

References:

Salesforce Einstein Trust Layer Overview: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer.htm

When previewing a prompt template in Salesforce, the Resolution text provides the response from the LLM (Large Language Model) based on the data from a sample record. This output shows what the AI model generated in response to the prompt, giving the AI Specialist a chance to review and adjust the response before finalizing the template.

Option B is correct because Resolution displays the actual response generated by the LLM.

Option A refers to sending the text to the Trust Layer, but that’s not what Resolution represents.

Option C relates to data masking, which is shown elsewhere, not under Resolution.

References:

Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm

To monitor Einstein Copilot's usability and the assignment of actions, the AI Specialist should run Einstein Copilot Analytics. This feature provides insights into how often Copilot is used, the types of actions it is handling, and overall user engagement with the system. It’s the most effective way to track Copilot's performance and usage patterns.

Platform Debug Logs are not relevant for tracking user behavior or the assignment of Copilot actions.

Querying the Copilot log data via the Metadata API would not provide the necessary insights in a structured manner.

For more details, refer to Salesforce's Copilot Analytics documentation for tracking AI-driven interactions.

Poor prompt performance when generating case summaries is often due to the data used for grounding being incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.

Option B (prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.

Option C (Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.

For more information, refer to Salesforce documentation on grounding AI models and data quality best practices.