FREE PDF 2025 SALESFORCE MARVELOUS AGENTFORCE-SPECIALIST: LATEST SALESFORCE CERTIFIED AGENTFORCE SPECIALIST BRAINDUMPS SHEET

Free PDF 2025 Salesforce Marvelous Agentforce-Specialist: Latest Salesforce Certified Agentforce Specialist Braindumps Sheet

Free PDF 2025 Salesforce Marvelous Agentforce-Specialist: Latest Salesforce Certified Agentforce Specialist Braindumps Sheet

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Salesforce Certified Agentforce Specialist Sample Questions (Q139-Q144):

NEW QUESTION # 139
Universal Containers wants to use an external large language model (LLM) in Prompt Builder.
What should An Agentforce recommend?

  • A. Use Apex to connect to an external LLM and ground the prompt.
  • B. Use BYO-LLM functionality in Einstein Studio.
  • C. Use Flow and External Services to bring data from an external LLM.

Answer: B

Explanation:
Bring Your Own Large Language Model (BYO-LLM) functionality in Einstein Studio allows organizations to integrate and use external large language models (LLMs) within the Salesforce ecosystem.
Universal Containers can leverage this feature to connect and ground prompts with external LLMs, allowing for custom AI model use cases and seamless integration with Salesforce data.
* Option B is the correct choice as Einstein Studio provides a built-in feature to work with external models.
* Option A suggests using Apex, but BYO-LLM functionality offers a more streamlined solution.
* Option C focuses on Flow and External Services, which is more about data integration and isn't ideal for working with LLMs.
References:
Salesforce Einstein Studio BYO-LLM Documentation: https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_llm.htm


NEW QUESTION # 140
Universal Containers (UC) implements a custom retriever to improve the accuracy of AI-generated responses.
UC notices that the retriever is returning too many irrelevant results, making the responses less useful. What should UC do to ensure only relevant data is retrieved?

  • A. Change the search index to a different data model object (DMO).
  • B. Define filters to narrow the search results based on specific conditions.
  • C. Increase the maximum number of results returned to capture a broader dataset.

Answer: B

Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, acustom retrieveris used to fetch relevant data (e.g., from Data Cloud's vector database or Salesforce records) to ground AI responses.
UC's issue is that their retriever returns too many irrelevant results, reducing response accuracy. The best solution is todefine filters(Option A) to refine the retriever's search criteria. Filters allow UC to specify conditions (e.g., "only retrieve documents from the 'Policy' category" or "records created after a certain date") that narrow the dataset, ensuring the retriever returns only relevant results. This directly improves the precision of AI-generated responses by excluding extraneous data, addressing UC's problem effectively.
* Option B: Changing the search index to a different data model object (DMO) might be relevant if the retriever is querying the wrong object entirely (e.g., Accounts instead of Policies). However, the question implies the retriever is functional but unrefined, so adjusting the existing setup with filters is more appropriate than switching DMOs.
* Option C: Increasing the maximum number of results would worsen the issue by returning even more data, including more irrelevant entries, contrary to UC's goal of improving relevance.
* Option A: Filters are a standard feature in custom retrievers, allowing precise control over retrieved data, making this the correct action.
Option A is the most effective step to ensure relevance in retrieved data.
References:
* Salesforce Agentforce Documentation: "Create Custom Retrievers" (Salesforce Help:https://help.
salesforce.com/s/articleView?id=sf.agentforce_custom_retrievers.htm&type=5)
* Salesforce Data Cloud Documentation: "Filter Data for AI Retrieval" (https://help.salesforce.com/s
/articleView?id=sf.data_cloud_retrieval_filters.htm&type=5)


NEW QUESTION # 141
Universal Containers (UC) wants to enable its sales team with automatic post-call visibility into mention of competitors, products, and other custom phrases.
Which feature should theAgentforce Specialistset up to enable UC's sales team?

  • A. Call Summaries
  • B. Call Explorer
  • C. Call Insights

Answer: C

Explanation:
To enable Universal Containers' sales team with automatic post-call visibility into mentions ofcompetitors, products, and custom phrases, theAgentforce Specialistshould set upCall Insights.Call Insightsanalyzes voice and video calls for key phrases, topics, and mentions, providing insights into critical aspects of the conversation. This feature automatically surfaces key details such as competitor mentions, product discussions, and custom phrases specified by the sales team.
* Call Summariesprovide a general overview of the call but do not specifically highlight keywords or topics.
* Call Exploreris a tool for navigating through call data but does not focus on automatic insights.
For more information, refer toSalesforce's Call Insights documentationregarding the analysis of call content and extracting actionable information.


NEW QUESTION # 142
Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should theAgentforce Specialistrecommend?

  • A. Service Replies and Work Summaries
  • B. Service Replies and Case Summaries
  • C. Reply Recommendations and Sales Summaries

Answer: B

Explanation:
To improve the productivity of the service center, theAgentforce Specialistshould recommend theService RepliesandCase Summariesfeatures.
* Service Replieshelps agents by automatically generating suggested responses to customer inquiries, reducing response time and improving efficiency.
* Case Summariesprovide a quick overview of case details, allowing agents to get up to speed faster on customer issues.
* Work Summariesare not as relevant for direct customer service operations, andSales Summariesare focused on sales processes, not service center productivity.
For more information, seeSalesforce's Einstein Service Cloud documentationon the use of generative AI to assist customer service teams.


NEW QUESTION # 143
How does the Einstein Trust Layer ensure that sensitive data is protected while generating useful and meaningful responses?

  • A. Masked data will be de-masked during request journey.
  • B. Responses that do not meet the relevance threshold will be automatically rejected.
  • C. Masked data will be de-masked during response journey.

Answer: C

Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de- masking it during the response journey.
How It Works:
* Data Masking in the Request Journey:
* Sensitive Data Identification:Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.
* Masking Sensitive Data:Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
* Processing by the LLM:
* Masked Input:The LLM processes the masked prompt and generates a response based on the masked data.
* No Exposure of Sensitive Data:Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
* De-masking in the Response Journey:
* Re-insertion of Sensitive Data:After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
* Providing Meaningful Responses:This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
* Maintaining Data Security:At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
Why Option A is Correct:
* De-masking During Response Journey:The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
* Balancing Security and Utility:This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
* Option B (Masked data will be de-masked during request journey):
* Incorrect Process:De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.
* Option C (Responses that do not meet the relevance threshold will be automatically rejected):
* Irrelevant to Data Protection:While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data.It addresses response quality rather than data security.
References:
* SalesforceAgentforce SpecialistDocumentation -Einstein Trust Layer Overview:
* Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.
* Salesforce Help -Data Masking and De-masking Process:
* Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.
* SalesforceAgentforce SpecialistExam Guide -Security and Compliance in AI:
* Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.


NEW QUESTION # 144
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