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NEW QUESTION # 174
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?
Answer: B
Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models
NEW QUESTION # 175
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization.
What should theAgentforce Specialistrecommend?
Answer: C
Explanation:
* Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.
* Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.
* Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus,Agent Analyticsis the correct recommendation as it offers actionable insights to drive agent adoption and productivity.
NEW QUESTION # 176
Universal Containers (UC) currently tracks Leads with a custom object. UC is preparing to implement the Sales Development Representative (SDR) Agent. Which consideration should UC keep in mind?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) uses a custom object for Leads and plans to implement the Agentforce Sales Development Representative (SDR) Agent. The SDR Agent is a prebuilt, configurable AI agent designed to assist sales teams by qualifying leads and scheduling meetings. Let's evaluate the options based on its functionality and limitations.
* Option A: Agentforce SDR only works with the standard Lead object.Per Salesforce documentation, the Agentforce SDR Agent is specifically designed to interact with thestandard Lead objectin Salesforce. It includes preconfigured logic to qualify leads, update lead statuses, and schedule meetings, all of which rely on standard Lead fields (e.g., Lead Status, Email, Phone). Since UC tracks leads in a custom object, this is a critical consideration-they would need to migrate data to the standard Lead object or create aworkaround (e.g., mapping custom object data to Leads) to leverage the SDR Agent effectively. This limitation is accurate and aligns with the SDR Agent's out-of-the-box capabilities.
* Option B: Agentforce SDR only works on Opportunities.The SDR Agent's primary focus is lead qualification and initial engagement, not opportunity management. Opportunities are handled by other roles (e.g., Account Executives) and potentially other Agentforce agents (e.g., Sales Agent), not the SDR Agent. This option is incorrect, as it misaligns with the SDR Agent's purpose.
* Option C: Agentforce SDR only supports custom objects associated with Accounts.There's no evidence in Salesforce documentation that the SDR Agent supports custom objects, even those related to Accounts. The SDR Agent is tightly coupled with the standard Lead object and does not natively extend to custom objects, regardless of their relationships. This option is incorrect.
Why Option A is Correct:The Agentforce SDR Agent's reliance on the standard Lead object is a documented constraint. UC must consider this when planning implementation, potentially requiring data migration or process adjustments to align their custom object with the SDR Agent's capabilities. This ensures the agent can perform its intended functions, such as lead qualification and meeting scheduling.
References:
* Salesforce Agentforce Documentation: SDR Agent Setup- Specifies the SDR Agent's dependency on the standard Lead object.
* Trailhead: Explore Agentforce Sales Agents- Describes SDR Agent functionality tied to Leads.
* Salesforce Help: Agentforce Prebuilt Agents- Confirms Lead object requirement for SDR Agent.
NEW QUESTION # 177
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language.
Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?
Answer: A
Explanation:
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, theAgentforce Specialistshould create aTrust Layer audit reportwithinData Cloud. By using the toxicity detector type filter, the report can displaytoxic responsesalong with their respective toxicity scores, allowingUniversal Containersto monitor and manage any toxic content generated with a high level of confidence.
* Option Cis correct because it enables visibility into toxic language detection within theTrust Layerand allows for auditing responses for toxicity.
* Option Asuggests checking a toxicity detection log, butSalesforceprovides more comprehensive options via the audit report.
* Option Binvolves creating a flow, which is unnecessary for toxicity detection monitoring.
References:
* Salesforce Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_audit.htm
NEW QUESTION # 178
Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases.
UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users.
When would Einstein Copilot implementation be most advantageous?
Answer: C
Explanation:
Einstein Copilotimplementation would be most advantageous inSalesforce Service Cloudwhen the goal is to streamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.
* Option B (data security)is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.
* Option C (marketing campaigns)falls outside the scope of Service Cloud and Einstein Copilot's primary benefits, which are aimed at improving customer service and case management.
For further reading, refer toSalesforce documentation on Einstein Copilot for Service Cloudand how it improves support processes.
NEW QUESTION # 179
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