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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Passing Score: | 73% |
| Real Exam Qty: | 60-65 |
| Certificate Validity Period: | 1 year (requires annual maintenance) |
| Exam Price: | $200 USD |
| Related Certifications: | Salesforce Certified AI Specialist |
| Exam Format: | Scenario-Based, Multiple Select, Multiple Choice |
| Available Languages: | English |
| Exam Duration: | 105 minutes |
| Recommended Training: | Trailhead Agentforce Specialist Certification Prep |
| Exam Registration: | Salesforce Certification Registration Pearson VUE Scheduling |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: 6 months experience with Agentforce or Salesforce AI features |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/certification-detail-print/?certificationId=286 |
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NEW QUESTION # 61
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: C
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 # 62
Universal Containers (UC) is expanding its Agentforce for Service capabilities to include case management.
For security purposes,
UC wants the agent to verify a customer's identity before providing any case-related information. The verification must be deterministic-ensuring that no case details are shared unless identity verification has been successfully completed.
Which approach best meets this requirement?
Answer: A
Explanation:
The AgentForce for Service Implementation Guide clearly outlines that when an agent must verify identity before performing any case-related operations, the correct method is to use a variable-based control flow. The documentation specifies: "To maintain deterministic and secure behavior, define a variable (for example,
'isVerified') that stores the result of an identity verification step. Use this variable as a conditional filter in the topic flow to ensure that case-related actions execute only when the variable equals 'true'." This ensures that no sensitive or case-specific data is shared unless verification is explicitly confirmed. It provides a deterministic safeguard, as the system only proceeds with case data actions after the verification variable confirms completion.
Option A ("Use keywords such as 'Always' and 'Never'") relies on natural language instructions, which are not deterministic and can be misinterpreted by the model. Option C ("Use a global instruction to check the variable") adds unnecessary complexity and lacks the control-level filtering that ensures secure flow logic.
Therefore, Option B correctly implements Salesforce's best-practice pattern for conditional execution using variables and filters in AgentForce.
References (AgentForce Documents / Study Guide):
AgentForce for Service Configuration Guide: "Identity Verification and Conditional Case Access" AgentForce Implementation Handbook: "Using Variables and Filters for Deterministic Agent Actions" AgentForce Study Guide: "Secure Flow Design in Service Agents"
NEW QUESTION # 63
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: C
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 # 64
Universal Containers has configured an agent to handle customer return requests. When a customer initiates a return, the agent must calculate a specific restocking fee. The agent needs to quote this exact fee to the customer and then reuse that same fee amount when summarizing the final refund. The Agentforce Specialist needs to ensure the agent uses deterministic logic to calculate the fee and consistently reuses the exact same value without guessing or hallucinating.
How should the specialist configure the agent to achieve this behavior?
Answer: C
Explanation:
The correct answer is B because deterministic fee calculation and reuse require state management, not conversational memory. The fee should be calculated by a Flow action, then stored in a context variable so the same validated value can be referenced later in the conversation. This prevents the large language model from recalculating, rounding differently, or hallucinating a new value during the refund summary. Option A is incomplete because simply exposing a Flow output once does not guarantee stable reuse throughout the session. Option C is unsafe because system instructions do not enforce calculation accuracy or persistence. Salesforce guidance states that variables can be used in filters, instructions, and action inputs, and Agent Script supports deterministic logic, setting variables, and running actions.
NEW QUESTION # 65
Universal Containers is rolling out a new generative AI initiative.
Which Prompt Builder limitations should the Agentforce Specialist be aware of?
Answer: B
Explanation:
When rolling out a new Generative AI initiative in Salesforce using Prompt Builder, it's important to understand its current limitations. One key limitation is that changes to prompt templates (creation, edits, or deletions) are not logged in the Setup Audit Trail, which means admins won't have a historical record of modifications for compliance or troubleshooting.
Reference:
"Prompt Builder Limitations | Salesforce Documentation" .
NEW QUESTION # 66
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