Agentforce-Specialist Practice Test Questions

Explanation

The primary function of the planner service in the Einstein Copilot system is to identify copilot actions that should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that the Einstein Copilot should perform based on user input.

Explanation:

Before enabling Call Explorer for meeting recordings, the AgentForce Specialist must ensure the following:

Einstein Conversation Insights Permission Set

Call Explorer is powered by Einstein Conversation Insights (ECI), which requires:
The "Einstein Conversation Insights" permission set assigned to users.
Meeting recordings (e.g., Zoom, Microsoft Teams) integrated with Salesforce.
Without this permission, users cannot access Call Explorer insights.

Why Not the Other Options?

A. "Call Explorer operates independently of Salesforce Knowledge":
Misleading. While Call Explorer doesn’t directly rely on Knowledge, it does require ECI setup and meeting data integration.

B. "Custom Call Explorer actions need to be built":
Incorrect. Call Explorer provides out-of-the-box insights (e.g., keyword detection, talk patterns). Custom actions are optional.

Steps to Enable Call Explorer:

1. Assign the "Einstein Conversation Insights" permission set to users.
2. Integrate meeting platforms (e.g., Zoom, Teams) with Salesforce.
3. Ensure recordings are synced to Salesforce (via Einstein Call Coaching or third-party tools).

This ensures sales teams can analyze call trends, coach reps, and extract AI-powered insights.

Explanation

Einstein Call Summaries is the best option for this scenario because it leverages Salesforce's AI capabilities to automatically summarize key details of video or voice calls. It includes details like deal terms, customer sentiments, follow-up tasks, and other crucial information. This feature is designed to help sales teams focus on their strategies rather than taking extensive manual notes during conversations.

Einstein Call Summaries: Automatically generates summaries for calls, identifying critical points such as next steps and follow-ups, enhancing efficiency and understanding of deal progression.

Einstein Conversation Insights: While it provides insights into customer sentiment and engagement, it is more suited for analyzing patterns across conversations rather than summarizing specific call details.

Einstein Video KPI: Focuses on analyzing key performance indicators within video calls but does not offer summarization features needed for deal terms or sentiment tracking. This feature ensures actionable insights are delivered directly into the Salesforce CRM, allowing sales managers to gain a concise overview without manually reviewing long recordings.

Explanation:

The Einstein Trust Layer provides enterprise-grade security for AI in Salesforce. For UC’s requirements, the best fit is:

Dynamic Grounding with Secure Data Retrieval
Safely pulls CRM data (e.g., Case/Account details) into prompts without exposing raw data to the LLM.
Uses real-time, permission-aware grounding to ensure only authorized fields are included (e.g., {{Record.Field}}).
Encrypts data in transit and adheres to Salesforce’s sharing model.

Why Not the Other Options?

A. Data Masking:
Hides sensitive data (e.g., PII) but doesn’t address dynamic record merging for prompts.

C. Zero-Data Retention:
Ensures LLM providers don’t store prompts, but doesn’t solve secure data retrieval from Salesforce records.

How It Works:

The Trust Layer dynamically injects record data into prompts (e.g., {{Account.Name}}).
Data is filtered by field-level security (FLS) and never stored externally.

This balances AI functionality with CRM data protection.

Explanation

When using related list merge fields in a prompt template associated with the Account object in Prompt Builder, the Activities related list is not supported due to it being a polymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.

Option B is incorrect because person accounts do not limit the availability of merge fields for the Account object.

Option C is irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.


For more information, refer to Salesforce documentation on supported fields and limitations in Prompt Builder.

Explanation:

When Einstein generates a Work Summary for cases, it dynamically grounds the summary in multiple data sources to ensure accuracy and relevance:

Conversation Context
Includes chat/email transcripts between the agent and customer.
Captures key details like customer intent, issues discussed, and resolutions proposed.

Knowledge Articles
References relevant Salesforce Knowledge articles linked to the case.
Ensures summaries align with approved solutions.

Case Data
Pulls structured case details (e.g., status, priority, custom fields).
Provides context like case history or related records (e.g., Account/Contact).

Why Not the Other Options?

B. "Existing conversation context only" → Too limited. Omits critical Knowledge and case data.
C. "Conversation context and Knowledge articles" → Misses structured case data, which is essential for summaries.

Explanation

For a sales rep who may miss key details during long sales calls, the Agentforce Specialist should recommend the Call Summary feature. Call Summary uses Einstein Generative AI to automatically generate a concise summary of important points discussed during the call, helping the rep quickly review the key information they might have missed.

Call Explorer is designed for manually searching through call data but doesn't summarize. Sales Summary is focused more on summarizing overall sales activity, not call-specific content.

For more details, refer to Salesforce's Call Summary documentation on how AI-generated summaries can improve sales rep productivity.

Explanation

When implementing Einstein Generative AI for improved customer insights and interactions, the Data Cloud is a key consideration for storing and managing large-scale audit and feedback data. The Salesforce Data Cloud(formerly known as Customer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioning Data Cloud, organizations like Universal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.

Audit and feedback data generated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and the Data Cloud provides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.

Custom objects or Salesforce Big Objects are not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions. Big Objects are more suited for archival data, whereas Data Cloud ensures more robust processing, segmentation, and analysis capabilities.

Explanation

Since security and data privacy are critical, the best option for the Agentforce Specialist is to integrate the fine- tuned LLM (Large Language Model)into Salesforce by adding it to Einstein Studio Model Builder. Einstein Studio allows organizations to bring their own AI models (BYOM), ensuring the model is securely managed within Salesforce’s environment, adhering to data privacy standards.

Option A (embedding via iFrame) is less secure and doesn’t integrate deeply with Salesforce's data and security models.

Option C (making callouts to OpenAI) raises concerns about data privacy, as sensitive Salesforce data would be sent to an external system.

Einstein Studio provides the most secure and seamless way to integrate custom AI models while maintaining control over data privacy and compliance. More details can be found in Salesforce's Einstein Studio documentation on integrating external models.

Explanation

Comprehensive and Detailed In-Depth Explanation: In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account.

The most robust and flexible way to achieve this is by using a Flow(Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by Created Date descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.

Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don’t natively support querying or aggregating related list data like the three most recent opportunities.

Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don’t directly query related objects without additional logic (e.g., a Flow), making this incorrect.

Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.

Thus, Option B is the correct method to gather the necessary data efficiently and accurately.