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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.

Topic 2

Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.

Topic 3

Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.

Topic 4

Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.

Topic 5

Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.

Salesforce Certified AI Specialist Exam Sample Questions (Q123-Q128):

NEW QUESTION # 123
What is an AI Specialist able to do when the "Enrich event logs with conversation data" setting in Einstein Copilot is enabled?

A. View the user click path that led to each copilot action.
B. Generate details reports on all Copilot conversations over any time period.
C. View session data including user Input and copilot responses for sessions over the past 7 days.

Answer: C

Explanation:
When the "Enrich event logs with conversation data" setting is enabled in Einstein Copilot, it allows an AI Specialist or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
* This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping AI specialists track AI behavior and user engagement.
* Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.
* Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.
Salesforce AI Specialist References:You can refer to this documentation for further insights: https://help.
salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm

NEW QUESTION # 124
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment.
Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?

A. Predictive model and generative model
B. Generative model
C. Predictive model

Answer: C

Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes.
Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision-making process.
* Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
* Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
Refer to Salesforce's Predictive AI model documentation for more insights on how predictive models are used for segmentation and decision making.

NEW QUESTION # 125
An AI Specialist has created a copilot custom action using flow as the reference action type. However, it is not delivering the expected results to the conversation preview, and therefore needs troubleshooting.
What should the AI Specialist do to identify the root cause of the problem?

A. Copilot Builder within the Dynamic Panel, confirm selected action and observe the values in Input and Output sections.
B. In Copilot Builder within the Dynamic Panel, turn on dynamic debugging to show the inputs and outputs.
C. In Copilot Builder, verify the utterance entered by the user and review session event logs for debug information.

Answer: B

Explanation:
When troubleshooting a copilot custom action using flow as the reference action type, enabling dynamic debugging within Copilot Builder's Dynamic Panel is the most effective way to identify the root cause. By turning on dynamic debugging, the AI Specialist can see detailed logs showing both the inputs and outputs of the flow, which helps identify where the action might be failing or not delivering the expected results.
Option B, confirming selected actions and observing the Input and Output sections, is useful for monitoring flow configuration but does not provide the deep diagnostic details available with dynamic debugging.
Option C, verifying the user utterance and reviewing session event logs, could provide helpful context, but dynamic debugging is the primary tool for identifying issues with inputs and outputs in real time.
Salesforce AI Specialist Reference:
To explore more about dynamic debugging in Copilot Builder, see: https://help.salesforce.com/s/articleView?id=sf.copilot_custom_action_debugging.htm

NEW QUESTION # 126
After a successful implementation of Agentforce Sates Agent with sales users. Universal Containers now aims to deploy it to the service team.
Which key consideration should the AI Specialist keep in mind for this deployment?

A. Review and test standard and custom Agent topics and actions for Service Center use cases.
B. Assign the Agentforce for Service permission to the Service Cloud users.
C. Assign the standard service actions to Agentforce Service Agent.

Answer: A

Explanation:
When deploying Einstein Agent (formerly Agentforce) from Sales to Service Cloud:
* Agent Topics and Actions are context-specific. Service Cloud use cases (e.g., case resolution, knowledge retrieval) require validation of existing topics/actions to ensure alignment with service workflows.
* Option A: Permissions like "Agentforce for Service" are necessary but secondary to functional compatibility.
* Option B: Standard service actions must be mapped to Agentforce, but testing ensures they function as intended.
References:
* Salesforce Help: Einstein Agent Setup
* Emphasizes reviewing "topics and actions for different user groups (Sales vs. Service)."

NEW QUESTION # 127
An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account.
Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?

A. Use merge fields to reference the default related list of opportunities.
B. Use formula fields to reference the Einstein related list of opportunities.
C. Use the merge fields to reference a custom related list of opportunities.

Answer: A

Explanation:
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the AI Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
* Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1
* Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en-us.salesforce_ai.meta
/salesforce_ai/grounding_data_prompts

NEW QUESTION # 128
......

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