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>> Agentforce-Specialist Exam Objectives <<
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NEW QUESTION # 184
In Model Playground, which hyperparameters of an existing
Salesforce-enabled foundational model can An Agentforce change?
Answer: A
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 # 185
Universal Containers needs a tool that can analyze voice and video call records to provide insights on competitor mentions, coaching opportunities, and other key information. The goal is to enhance the team's performance by identifying areas for improvement and competitive intelligence.
Which feature provides insights about competitor mentions and coaching opportunities?
Answer: B
Explanation:
For analyzing voice and video call records to gain insights into competitor mentions, coaching opportunities, and other key information,Call Exploreris the most suitable feature.Call Explorer, a part ofEinstein Conversation Insights, enables sales teams to analyze calls, detect patterns, and identify areas where improvements can be made. It uses natural language processing (NLP) to extract insights, including competitor mentionsand moments for coaching. These insights are vital for improving sales performance by providing a clear understanding of the interactions during calls.
* Call Summariesoffer a quick overview of a call but do not delve deep into competitor mentions or coaching insights.
* Einstein Sales Insightsfocuses more on pipeline and forecasting insights rather than call-based analysis.
References:
* Salesforce Einstein Conversation Insights Documentation:https://help.salesforce.com/s/articleView?
id=einstein_conversation_insights.htm
NEW QUESTION # 186
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should An Agentforce recommend to resolve the issue?
Answer: B
Explanation:
When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the Agentforce Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist References:For more information, refer to: https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer_data_masking.htm
NEW QUESTION # 187
Universal Containers (UC) wants to offer personalized service experiences and reduce agent handling time with Al-generated email responses, grounded in Knowledge base.
Which AI capability should UC use?
Answer: C
Explanation:
ForUniversal Containers (UC)to offer personalized service experiences and reduce agenthandling time using AI-generated responses grounded in theKnowledge base, the best solution isEinstein Service Replies for Email. This capability leverages AI to automatically generate responses to service-related emails based on historical data and theKnowledge base, ensuring accuracy and relevance while saving time for service agents.
* Einstein Email Replies(option A) is more suited for sales use cases.
* Einstein Generative Service Replies for Email(option C) could be a future offering, but as of now, Einstein Service Replies for Emailis the correct choice for grounded, knowledge-based responses.
References:
Einstein Service Replies Overview:
NEW QUESTION # 188
After creating a foundation model in Einstein Studio, which hyperparameter should An Agentforce use to adjust the balance between consistency and randomness of a response?
Answer: A
Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
* Low Temperature (e.g., 0.2): More deterministic, consistent responses.
* High Temperature (e.g., 1.0): More creative, varied responses.
* Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
* Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
* Einstein Studio Documentation: Model Hyperparameters
* Explicitly states "Temperature adjusts the balance between predictable and random outputs."
NEW QUESTION # 189
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