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  1. Home
  2. Salesforce Certification
  3. Agentforce-Specialist Exam
  4. Salesforce.Agentforce-Specialist.v2025-09-29.q108 Dumps
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Question 6

Universal Containers (UC) has implemented Generative AI within Salesforce to enable summarization of a custom object called Guest. Users have reported mismatches in the generated information.
In refining its prompt design strategy, which key practices should UC prioritize?

Correct Answer: B
ForUniversal Containers (UC)to refine itsGenerative AIprompt design strategy and improve the accuracy of the generated summaries for the custom objectGuest, the best practice is to focus on craftingconcise, clear, and consistent prompt templates.This includes:
* Effective grounding: Ensuring the prompt pulls data from the correct sources.
* Contextual role-playing: Providing the AI with a clear understanding of its role in generating the summary.
* Clear instructions: Giving unambiguous directions on what to include in the response.
* Iterative feedback: Regularly testing and adjusting prompts based on user feedback.
* Option Bis correct because it follows industry best practices for refining prompt design.
* Option A(prompt test mode) is useful but less relevant for refining prompt design itself.
* Option C(prompt review case with Salesforce) would be more appropriate for technical issues or complex prompt errors, not general design refinement.
:
Salesforce Prompt Design Best Practices:https://help.salesforce.com/s/articleView?id=sf.
prompt_design_best_practices.htm
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Question 7

An Agentforce Specialist is tasked with analyzing Agent interactions, looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the Agentforce Specialist to achieve this?

Correct Answer: C
Comprehensive and Detailed In-Depth Explanation:The task requires analyzinguser inputs, requests, and queriesto identify patterns and trends in Agentforce interactions. Let's assess the options based on Agentforce' s analytics capabilities.
* Option A: Agent Event Logs dashboard.Agent Event Logs capture detailed technical events (e.g., API calls, errors, or system-level actions) related to agent operations. While useful for troubleshooting or monitoring system performance, they are not designed to analyze user inputs or conversational trends. This option does not meet the requirement and is incorrect.
* Option B: AI Audit and Feedback Data dashboard.There's no specific "AI Audit and Feedback Data dashboard" in Agentforce documentation. Feedback mechanisms exist (e.g., user feedback on responses), and audit trails may track changes, but no single dashboard combines these for analyzing user queries and trends. This option appears to be a misnomer and is incorrect.
* Option C: User Utterances dashboard.The User Utterances dashboard in Agentforce Analytics is specifically designed to analyze user inputs, requests, and queries. It aggregates and visualizes what users are asking the agent, identifying patterns (e.g., common topics) and trends (e.g., rising query types). Specialists can use this to refine agent instructions or topics, making it the perfect tool for this task. This is the correct answer per Salesforce documentation.
Why Option C is Correct:The User Utterances dashboard is tailored for conversational analysis, offering insights into user interactions that align with the specialist's goal of identifying patterns and trends. It's a documented feature of Agentforce Analytics for post-deployment optimization.
References:
* Salesforce Agentforce Documentation: Agent Analytics > User Utterances Dashboard- Describes its use for analyzing user queries.
* Trailhead: Monitor and Optimize Agentforce Agents- Highlights the dashboard's role in trend identification.
* Salesforce Help: Agentforce Dashboards- Confirms User Utterances as a key tool for interaction analysis.
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Question 8

Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft email contains placeholders for attributes that could have been derived from the recipient's contact record. What is the most likely explanation for why the draft email shows these placeholders?

Correct Answer: A
Comprehensive and Detailed In-Depth Explanation:
UC is using an Einstein Generative AI feature (likely Einstein Sales Emails) to draft personalized emails, but placeholders (e.g., {!Contact.FirstName}) appear instead of actual data from the contact record. Let's analyze the options.
* Option A: The user does not have permission to access the fields.Einstein Sales Emails, built on Prompt Builder, pulls data from contact records to populate email drafts. If the user lacks field-level security (FLS) or object-level permissions to access relevant fields (e.g., FirstName, Email), the system cannot retrieve the data, leaving placeholders unresolved. This is a common issue in Salesforce when permissions restrict data access, making it the most likely explanation and the correct answer.
* Option B: The user's locale language is not supported by Prompt Builder.Prompt Builder and Einstein Sales Emails support multiple languages, and locale mismatches typically affect formatting or translation, not data retrieval. Placeholders appearing instead of data isn't a documented symptom of language support issues, making this unlikely and incorrect.
* Option C: The user does not have Einstein Sales Emails permission assigned.The Einstein Sales Emails permission (part of the Einstein Generative AI license) enables the feature itself. If missing, users couldn't generate drafts at all-not just see placeholders. Since drafts are being created, this permission is likely assigned, making this incorrect.
Why Option A is Correct:
Permission restrictions are a frequent cause of unresolved placeholders in Salesforce AI features, as the system respects FLS and sharing rules. This is well-documented in troubleshooting guides for Einstein Generative AI.
References:
Salesforce Help: Einstein Sales Emails > Troubleshooting- Lists permissions as a cause of data issues.
Trailhead: Set Up Einstein Generative AI- Emphasizes field access for personalization.
Agentforce Documentation: Prompt Builder > Data Access- Notes dependency on user permissions.
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Question 9

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?

Correct Answer: C
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.
:
Salesforce Einstein Conversation Insights Documentation:https://help.salesforce.com/s/articleView?
id=einstein_conversation_insights.htm
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Question 10

Universal Containers (UC) would like to implement the Sales Development Representative (SDR) Agent.
Which channel consideration should UC be aware of while implementing it?

Correct Answer: A
Comprehensive and Detailed In-Depth Explanation:
Universal Containers (UC) is implementing the Agentforce Sales Development Representative (SDR) Agent, a prebuilt AI agent designed to qualify leads and schedule meetings. Channel considerations are critical for deployment. Let's evaluate the options based on official Salesforce documentation.
* Option A: SDR Agent must be deployed in the Messaging channel.The Agentforce SDR Agent is designed to engage prospects in real-time conversations, primarily through the Messaging channel (e.g., Salesforce Messaging for in-app or web chat). This aligns with its purpose of qualifying leads interactively and scheduling meetings, as outlined in Agentforce for Sales documentation. While it may leverage email for follow-ups, its core deployment and interaction occur via Messaging, making this a key consideration UC must be aware of. This is the correct answer.
* Option B: SDR Agent only works in the Email channel.The SDR Agent is not limited to email.
While it can send emails (e.g., follow-ups after lead qualification), its primary function-real-time lead engagement-relies on Messaging. Stating it "only works in the Email channel" is inaccurate and contradicts its documented capabilities, making this incorrect.
* Option C: SDR Agent must also be deployed on the company website.While the SDR Agent can be embedded on a company website via Messaging (e.g., as a chat widget), this is an implementation choice, not a mandatory requirement. The agent's deployment is channel-specific (Messaging), and website integration is optional, not a "must." This option overstates the requirement, making it incorrect.
Why Option A is Correct:
The SDR Agent's primary deployment in the Messaging channel is a documented consideration for its real- time lead qualification capabilities. UC must plan for this channel to ensure effective implementation, as per Salesforce guidelines.
References:
Salesforce Agentforce Documentation: SDR Agent Setup > Channels- Specifies Messaging as the primary channel.
Trailhead: Explore Agentforce Sales Agents- Notes SDR Agent's Messaging focus for lead engagement.
Salesforce Help: Agentforce for Sales > SDR Agent- Confirms Messaging deployment requirement.
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