AI Specialist Agentforce-Specialist Passing Score

In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.

Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.

Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.

Salesforce References:

Merge Fields in Templates: &type=1

Grounding Data in Prompts:

The Einstein Trust Layer is designed to ensure responsible and compliant AI usage. Data Masking (B) is the mechanism that directly addresses compliance with data protection regulations like GDPR by obscuring or anonymizing sensitive personal data (e.g., names, emails, phone numbers) before it is processed by AI models. This prevents unauthorized exposure of personally identifiable information (PII) and ensures adherence to privacy laws.

Salesforce documentation explicitly states that Data Masking is a core component of the Einstein Trust Layer, enabling organizations to meet GDPR requirements by automatically redacting sensitive fields during AI interactions. For example, masked data ensures that PII is not stored or used in AI model training or inference without explicit consent.

In contrast:

Toxicity Scoring (A) identifies harmful or inappropriate content in outputs but does not address data privacy.

Prompt Defense (C) guards against malicious prompts or injection attacks but focuses on security rather than data protection compliance.

The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the Agentforce Specialist to:

Identify Common Queries: Surface frequently asked questions or recurring issues.

Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.

Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.

Why Other Options Are Incorrect:

B. Agent Event Logs dashboard: Focuses on agent activity (e.g., response times, resolved cases) rather than user input analysis.

C. AI Audit & Feedback Data dashboard: Tracks AI model performance, audit trails, and user feedback scores but does not directly analyze raw user utterances or queries.

Salesforce Einstein Agentforce Specialist Certification Guide: Emphasizes the User Utterances dashboard as the primary tool for analyzing user inputs to improve conversational AI.

Trailhead Module: "Einstein Bots Basics" highlights using the dashboard to refine bot training based on user interaction data.

Salesforce Help Documentation: Describes the User Utterances dashboard as critical for identifying trends in customer interactions.

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.

Salesforce Einstein Trust Layer Overview: