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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist |
| Exam Number: | Agentforce-Specialist |
| Available Languages: | English |
| Exam Format: | Multiple-select, Multiple-choice |
| Exam Duration: | 105 minutes |
| Exam Price: | USD 200 |
| Passing Score: | 73% |
| Real Exam Qty: | 60 |
| Related Certifications: | Salesforce Certified AI Associate Salesforce Certified AI Specialist (Legacy) |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online (Proctored) or Testing Center |
| Pre Condition: | None |
| Official Syllabus URL: | https://trailhead.salesforce.com/help?article=Salesforce-Certified-Agentforce-Specialist-Exam-Guide |
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NEW QUESTION # 70
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
Answer: C
Explanation:
* Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
* Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
* Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus,Service Repliesis the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
Reference:
"Boost Productivity with Generative AI in Service Cloud | Salesforce Trailhead" .
NEW QUESTION # 71
Universal Containers (UC) recently rolled out Einstein Generative AI capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information. What is a possible explanation for the poor prompt performance?
Answer: A
Explanation:
UC's custom prompt for summarizing case records is underperforming, and we need to identify a likely cause.
Let's evaluate the options based on Agentforce and Einstein Generative AI mechanics.
* Option A: The prompt template version is incompatible with the chosen LLM.Prompt templates in Agentforce are designed to work with the Atlas Reasoning Engine, which abstracts the underlying large language model (LLM). Salesforce manages compatibility between prompt templates and LLMs, and there's no user-facing versioning that directly ties to LLM compatibility. This option is unlikely and not a common issue per documentation.
* Option B: The data being used for grounding is incorrect or incomplete.Grounding is the process of providing context (e.g., case record data) to the AI via prompt templates. If the grounding data- sourced from Record Snapshots, Data Cloud, or other integrations-is incorrect (e.g., wrong fields mapped) or incomplete (e.g., missing key case details), the summaries will be inaccurate. For example, if the prompt relies on Case.Subject but the field is empty or not included, the output will miss critical information. This is a frequent cause of poor performance in generative AI and aligns with Salesforce troubleshooting guidance, making it the correct answer.
* Option C: The Einstein Trust Layer is incorrectly configured.The Einstein Trust Layer enforces guardrails (e.g., toxicity filtering, data masking) to ensure safe and compliant AI outputs.
Misconfiguration might block content or alter tone, but it's unlikely to cause summaries to lack appropriate information unless specific fields are masked unnecessarily. This is less probable than grounding issues and not a primary explanation here.
Why Option B is Correct:
Incorrect or incomplete grounding data is a well-documented reason for subpar AI outputs in Agentforce. It directly affects the quality of case summaries, and specialists are advised to verify grounding sources (e.g., field mappings, Data Cloud queries) when troubleshooting, as per official guidelines.
References:
Salesforce Agentforce Documentation: Prompt Templates > Grounding - Links poor outputs to grounding issues.
Trailhead: Troubleshoot Agentforce Prompts - Lists incomplete data as a common problem.
Salesforce Help: Einstein Generative AI > Debugging Prompts - Recommends checking grounding data first.
NEW QUESTION # 72
Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should theAgentforce Specialistrecommend?
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 # 73
An Agentforce is setting up a new org and needs to ensure that users can create and execute prompt templates.
TheAgentforce Specialistis unsure which roles are necessary for these tasks.
Which permission sets should theAgentforce Specialistassign to users who need to create and execute prompt templates?
Answer: C
Explanation:
To effectively manage and use prompt templates, two distinct permission sets are required:
* Prompt Template Manager: This permission set allows users to create prompt templates. It provides the necessary access to define templates, which can be shared and utilized across the organization.
* Prompt Template User: This permission set is designed for users who need to execute the templates. It provides the ability to interact with pre-designed prompts and generate outcomes based on these templates.
TheData Cloud Adminpermission set is not directly relevant to creating or executing prompt templates but is more focused on managing the Data Cloud.
NEW QUESTION # 74
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the correct order.
Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner service to work as expected?
Answer: C
Explanation:
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, theAgentforce Specialistmust explicitly specify dependent actions using their API names in the custom action's configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A's API name must be listed as a dependency in Action B's instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.
In contrast:
* Profiles or custom permissions (B) control access to the action but do not influence execution order.
* LLM model provider and version (C) determine the AI model used for processing but are unrelated to the planner's sequencing logic.
NEW QUESTION # 75
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