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Salesforce Certified Agentforce Specialist Sample Questions (Q72-Q77):

NEW QUESTION # 72
A SalesforceAgentforce Specialistis reviewing the feedback from a customer about the ineffectiveness of the prompt template.
What should theAgentforce Specialistdo to ensure the prompt template's effectiveness?

A. Use the Prompt Builder Scorecard to help monitor.
B. Periodically change the templates grounding object.
C. Monitor and refine the template based on user feedback.

Answer: A

Explanation:
To address the ineffectiveness of a prompt template reported by a customer, the SalesforceAgentforce Specialistshould use the Prompt Builder Scorecard (Option B). This tool is explicitly designed to evaluate and monitor prompt templates against key criteria such as relevance, accuracy, safety, and grounding. By leveraging the scorecard, the specialist can systematically identify weaknesses in the template and make data- driven refinements. While monitoring and refining based on user feedback (Option A) is a general best practice, the Prompt Builder Scorecard is Salesforce's recommended tool for structured evaluation, aligning with documented processes for maintaining prompt effectiveness. Changing the grounding object (Option C) without proper evaluation is reactive and does not address the root cause.
References:
* Salesforce EinsteinAgentforce SpecialistCertification Guide: Emphasizes using the Prompt Builder Scorecard to evaluate prompts and iterate based on results.
* Trailhead Module: "Einstein for Developers" highlights the scorecard as a critical tool for assessing prompt performance.
* Salesforce Help Documentation: Details the Scorecard's role in evaluating prompts against predefined criteria.

NEW QUESTION # 73
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities. What is one criterion that Agentforce for Sales uses to match similar opportunities?

A. Matched opportunities are limited to the same account.
B. Matched opportunities were created in the last 12 months.
C. Matched opportunities have a status of Closed Won from the last 12 months.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure. Let's determine a criterion used by the "Find Similar Opportunities" feature.
* Option A: Matched opportunities have a status of Closed Won from the last 12 months.Agentforce for Sales analyzes historical data to find similar opportunities, prioritizing "Closed Won" deals as successful examples. Documentation specifies a 12-month lookback period for relevance, ensuring recent, applicable matches. This is a key criterion, making it the correct answer.
* Option B: Matched opportunities are limited to the same account.While account context may factor in, Agentforce doesn't restrict matches to the same account-it considers broader patterns across opportunities (e.g., industry, deal size). This is too narrow and incorrect.
* Option C: Matched opportunities were created in the last 12 months.Creation date isn't a primary criterion-status (e.g., Closed Won) and recency of closure matter more. This doesn't align with documented behavior, making it incorrect.
Why Option A is Correct:"Closed Won" status within 12 months is a documented criterion for Agentforce's similarity matching, providing actionable insights for deal closure.
References:
* Salesforce Agentforce Documentation: Agentforce for Sales > Find Similar Opportunities- Specifies Closed Won, 12-month criterion.
* Trailhead: Explore Agentforce Sales Agents- Details opportunity matching logic.
* Salesforce Help: Sales Features in Agentforce- Confirms historical success focus.

NEW QUESTION # 74
A data science team has trained an XGBoost classification model for product recommendations on Databricks. TheAgentforce Specialistis tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should theAgentforce Specialistset this up?

A. Create the serving endpoint in Databricks, then configure the model using Model Builder.
B. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
C. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.

Answer: A

Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, theAgentforce Specialistshould configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.

NEW QUESTION # 75
How does an Agent respond when it can't understand the request or find any requested information?

A. With a general message asking the user to rephrase the request.
B. With a generated error message.
C. With a preconfigured message, based on the action type.

Answer: A

Explanation:
Comprehensive and Detailed In-Depth Explanation:Agentforce Agents are designed to handle situations where they cannot interpret a request or retrieve requested data gracefully. Let's assess the options based on Agentforce behavior.
* Option A: With a preconfigured message, based on the action type.While Agentforce allows customization of responses, there's no specific mechanism tying preconfigured messages to action types for unhandled requests. Fallback responses are more general, not action-specific, making this incorrect.
* Option B: With a general message asking the user to rephrase the request.When an Agentforce Agent fails to understand a request or find information, it defaults to a general fallback response, typically asking the user to rephrase or clarify their input (e.g., "I didn't quite get that-could you try asking again?"). This is configurable in Agent Builder but defaults to a user-friendly prompt to encourage retry, aligning with Salesforce's focus on conversational UX. This is the correct answer per documentation.
* Option C: With a generated error message.Agentforce Agents prioritize user experience over technical error messages. While errors might log internally (e.g., in Event Logs), the user-facing response avoids jargon and focuses on retry prompts, making this incorrect.
Why Option B is Correct:The default behavior of asking users to rephrase aligns with Agentforce's conversational design principles, ensuring a helpful response when comprehension fails, as noted in official resources.
References:
* Salesforce Agentforce Documentation: Agent Builder > Fallback Responses- Describes general retry messages.
* Trailhead: Build Agents with Agentforce- Covers handling ununderstood requests.
* Salesforce Help: Agentforce Interaction Design- Confirms user-friendly fallback behavior.

NEW QUESTION # 76
In Model Playground, which hyperparameters of an existing
Salesforce-enabled foundational model can An Agentforce change?

A. Temperature, Top-k sampling, Presence Penalty
B. Temperature, Frequency Penalty, Output Tokens
C. Temperature, Frequency Penalty, Presence Penalty

Answer: C

Explanation:
InModel Playground, An Agentforce working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
* Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
* Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
* Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer toSalesforce AI Model Playgroundguidance from Salesforce's official documentation on foundational model adjustments.

NEW QUESTION # 77
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