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Salesforce-AI-Specialist Exam Questions Answers | Exam Salesforce-AI-Specialist Lab Questions

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Exam Salesforce-AI-Specialist Lab Questions, Salesforce-AI-Specialist Exam Cram

The Salesforce Certified AI Specialist Exam has become very significant to validate expertise and level up career. Success in the Salesforce Certified AI Specialist Exam exam helps you meet the ever-changing dynamics of the tech industry. latest Salesforce Certified AI Specialist Exam Salesforce-AI-Specialist Exam Cram Pdf, collection pdf and exam dumps have been provided in Pass4cram. With 365 days updates.

Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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.

Topic 3

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 4

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 5

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.

Salesforce Certified AI Specialist Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
Universal Containers (UC) uses Salesforce Service Cloud to support its customers and agents handling cases.
UC is considering implementing Einstein Copilot and extending Service Cloud to mobile users.
When would Einstein Copilot implementation be most advantageous?

A. When the main objective is to enhance data security and compliance measures
B. When the goal is to streamline customer support processes and improve response times
C. When the focus is on optimizing marketing campaigns and strategies

Answer: B

Explanation:
Einstein Copilotimplementation would be most advantageous inSalesforce Service Cloudwhen the goal is to streamline customer support processes and improve response times. Einstein Copilot can assist agents by providing real-time suggestions, automating repetitive tasks, and generating contextual responses, thus enhancing service efficiency.
* Option B (data security)is not the primary focus of Einstein Copilot, which is more about improving operational efficiency.
* Option C (marketing campaigns)falls outside the scope of Service Cloud and Einstein Copilot's primary benefits, which are aimed at improving customer service and case management.
For further reading, refer toSalesforce documentation on Einstein Copilot for Service Cloudand how it improves support processes.

NEW QUESTION # 72
An AI Specialist configured Data Masking within the Einstein Trust Layer.
How should the AI Specialist begin validating that the correct fields are being masked?

A. Request the Einstein Generative AI Audit Data from the Security section of the Setup menu.
B. Enable the collection and storage of Einstein Generative AI Audit Data on the Einstein Feedback setup page.
C. Use a Flow-based resource in Prompt Builder to debug the fields' merge values using Flow Debugger.

Answer: A

Explanation:
To begin validating that the correct fields are being masked in Einstein Trust Layer, the AI Specialist should request the Einstein Generative AI Audit Data from the Security section of the Salesforce Setup menu. This audit data allows the AI Specialist to see how data is being processed, including which fields are being masked, providing transparency and validation that the configuration is working as expected.
Option B is correct because it allows for the retrieval of audit data that can be used to validate data masking.
Option A (Flow Debugger) and Option C (Einstein Feedback) do not relate to validating field masking in the context of the Einstein Trust Layer.
Reference:
Salesforce Einstein Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm

NEW QUESTION # 73
Universal Containers (UC) is Implementing Service AI Grounding to enhance its customer service operations. UC wants to ensure that its AI- generated responses are grounded in the most relevant data sources. The team needs to configure the system to include all supported objects for grounding.
Which objects should UC select to configure Service AI Grounding?

A. Case, Case Emails, and Knowledge
B. Case, Knowledge, and Case Notes
C. Case and Knowledge

Answer: C

Explanation:
Universal Containers (UC) is implementing Service AI Grounding to enhance its customer service operations. They aim to ensure that AI-generated responses are grounded in the most relevant data sources and need to configure the system to include all supported objects for grounding.
Supported Objects for Service AI Grounding:
Case
Knowledge
Case Object:
Role in Grounding: Provides contextual data about customer inquiries, including case details, status, and history.
Benefit: Grounding AI responses in case data ensures that the information provided is relevant to the specific customer issue being addressed.
Knowledge Object:
Role in Grounding: Contains articles and documentation that offer solutions and information related to common issues.
Benefit: Utilizing Knowledge articles helps the AI provide accurate and helpful responses based on verified information.
Exclusion of Other Objects:
Case Notes and Case Emails:
Not Supported for Grounding: While useful for internal reference, these objects are not included in the supported objects for Service AI Grounding.
Reason: They may contain sensitive or unstructured data that is not suitable for AI grounding purposes.
Why Options A and C are Incorrect:
Option A (Case, Knowledge, and Case Notes):
Case Notes Not Supported: Case Notes are not among the supported objects for grounding in Service AI.
Option C (Case, Case Emails, and Knowledge):
Case Emails Not Supported: Case Emails are also not included in the list of supported objects for grounding.
Reference:
Salesforce AI Specialist Documentation - Service AI Grounding Configuration: Details the objects supported for grounding AI responses in Service Cloud.
Salesforce Help - Implementing Service AI Grounding: Provides guidance on setting up grounding with Case and Knowledge objects.
Salesforce Trailhead - Enhance Service with AI Grounding: Offers an interactive learning path on using AI grounding in service scenarios.

NEW QUESTION # 74
An AI Specialist 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?

A. Specify the profiles or custom permissions allowed to invoke the action.
B. Specify the dependent actions with the reference to the action API name.
C. Specify the LLM model provider and version to be used to invoke the action.

Answer: B

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, the AISpecialist must 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
When configuring a prompt template, an AI Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response.
Which information does the Resolution text provide?

A. It shows which sensitive data is masked before it is sent to the LLM.
B. It shows the full text that is sent to the Trust Layer.
C. It shows the response from the LLM based on the sample record.

Answer: C

Explanation:
When previewing a prompt template in Salesforce, the Resolution text provides the response from the LLM (Large Language Model) based on the data from a sample record. This output shows what the AI model generated in response to the prompt, giving the AI Specialist a chance to review and adjust the response before finalizing the template.
Option B is correct because Resolution displays the actual response generated by the LLM.
Option A refers to sending the text to the Trust Layer, but that's not what Resolution represents.
Option C relates to data masking, which is shown elsewhere, not under Resolution.
Reference:
Salesforce Prompt Builder Overview: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm

NEW QUESTION # 76
......

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