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Salesforce Agentforce-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

Agentforce and Sales Cloud: This section assesses the skills of AI Developers and covers identifying the correct generative AI features in Agentforce for Sales Cloud scenarios. It also includes determining when to use Agentforce Sales Agents, such as Sales Development Representatives (SDRs) and Sales Coaches.

Topic 2

Agentforce Concepts: This section assesses the skills of AI Engineers and covers how Agentforce works, including its reasoning engine, standard and custom topics, agent actions, and user security management. It also includes testing and deploying agents from sandbox to production environments.

Topic 3

Agentforce and Data Cloud: This section measures the skills of AI Developers and addresses how Agentforce integrates with Data Cloud to improve response accuracy and personalize answers. It involves grounding with retrievers in Data Cloud to enhance agent performance.

Topic 4

Agentforce and Service Cloud: This section measures the skills of AI Engineers and focuses on building agents that answer questions based on Knowledge articles and connecting them to digital channels. It also covers identifying the correct generative AI features in Agentforce for Service Cloud scenarios.

Topic 5

Prompt Engineering: This section measures the skills of AI Developers and focuses on prompt engineering techniques. It covers identifying when to use Prompt Builder, managing prompt templates, selecting appropriate grounding techniques, and explaining the process for creating and executing prompt templates.

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Salesforce Certified Agentforce Specialist Sample Questions (Q32-Q37):

NEW QUESTION # 32
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
In the score of 1 indicate?

A. The response is the most toxic.
B. The response is not toxic.
C. The response is the least toxic Einstein Generative AI Toxicity Scoring system, what does a toxicity category.

Answer: A

Explanation:
Einstein Trust Layer's Toxicity Scoring categorizes content on a scale of 0 to 1, where 1 indicates the highest level of toxicity (e.g., harmful, biased, or inappropriate language). This scoring helps organizations filter unsafe AI-generated content. A score of 1 triggers mitigation actions, such as blocking the response or alerting administrators.
* A score of 0 would indicate no toxicity (B is incorrect).
* The scoring system does not use "least toxic" as a category (A is misleading).
Reference:
Salesforce Help Article: Einstein Trust Layer - Toxicity Scoring ("Interpreting Toxicity Scores" section).
Einstein GPT Safety Overview: "Mitigating Harmful Content with Toxicity Detection."

NEW QUESTION # 33
Universal Containers (UC) wants to assess Salesforce's generative features but has concerns over its company data being exposed to third- party large language models (LLMs). Specifically, UC wants the following capabilities to be part of Einstein's generative AI service.
No data is used for LLM training or product improvements by third- party LLMs.
No data is retained outside of UC's Salesforce org.
The data sent cannot be accessed by the LLM provider.
Which property of the Einstein Trust Layer should theAgentforce Specialisthighlight to UC that addresses these requirements?

A. Data Masking
B. Zero-Data Retention Policy
C. Prompt Defense

Answer: B

Explanation:
Universal Containers (UC)has concerns about data privacy when usingSalesforce's generative AIfeatures, particularly around preventing third-party LLMs from accessing or retaining their data. TheZero-Data Retention Policyin theEinstein Trust Layeris designed to address these concerns by ensuring that:
* No data is used for trainingor product improvements by third-party LLMs.
* No data is retainedoutside of the customer's Salesforce organization.
* The LLM provider cannot access any customer data.
This policy aligns perfectly with UC's requirements for keeping their data safe while leveraging generative AI capabilities.
* Prompt DefenseandData Maskingare also security features, but they do not directly address the concerns related to third-party data access and retention.
References:
* Salesforce Einstein Trust Layer Documentation:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer.htm

NEW QUESTION # 34
Universal Containers is using Agentforce for Sales to find similar opportunities to help close deals faster. The team wants to understand the criteria used by the Agent to match opportunities. What is one criterion that Agentforce for Sales uses to match similar opportunities?

A. Matched opportunities were created in the last 12 months.
B. Matched opportunities are limited to the same account.
C. Matched opportunities have a status of Closed Won from the last 12 months.

Answer: C

Explanation:
Comprehensive and Detailed In-Depth Explanation:UC uses Agentforce for Sales to identify similar opportunities, aiding deal closure. Let's determine a criterion used by the "Find Similar Opportunities" feature.
* Option A: Matched opportunities have a status of Closed Won from the last 12 months.Agentforce for Sales analyzes historical data to find similar opportunities, prioritizing "Closed Won" deals as successful examples. Documentation specifies a 12-month lookback period for relevance, ensuring recent, applicable matches. This is a key criterion, making it the correct answer.
* Option B: Matched opportunities are limited to the same account.While account context may factor in, Agentforce doesn't restrict matches to the same account-it considers broader patterns across opportunities (e.g., industry, deal size). This is too narrow and incorrect.
* Option C: Matched opportunities were created in the last 12 months.Creation date isn't a primary criterion-status (e.g., Closed Won) and recency of closure matter more. This doesn't align with documented behavior, making it incorrect.
Why Option A is Correct:"Closed Won" status within 12 months is a documented criterion for Agentforce's similarity matching, providing actionable insights for deal closure.
References:
* Salesforce Agentforce Documentation: Agentforce for Sales > Find Similar Opportunities- Specifies Closed Won, 12-month criterion.
* Trailhead: Explore Agentforce Sales Agents- Details opportunity matching logic.
* Salesforce Help: Sales Features in Agentforce- Confirms historical success focus.

NEW QUESTION # 35
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?

A. Einstein Generative Service Replies for Email
B. Einstein Service Replies for Email
C. Einstein Email Replies

Answer: B

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 # 36
What is best practice when refining Einstein Copilot custom action instructions?

A. Specify the persona who will request the action.
B. Provide examples of user messages that are expected to trigger the action.
C. Use consistent introductory phrases and verbs across multiple action instructions.

Answer: B

Explanation:
When refiningEinstein Copilot custom action instructions, it is considered best practice toprovide examples of user messagesthat are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
* Option B(consistent phrases) can improve clarity but does not directly refine the triggering logic.
* Option C(specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.
For more details, refer toSalesforce's Einstein Copilot documentationon building and refining custom actions.

NEW QUESTION # 37
......

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