SALESFORCE-AI-SPECIALIST EXAM TIPS, EXAMCOLLECTION SALESFORCE-AI-SPECIALIST FREE DUMPS

Salesforce-AI-Specialist Exam Tips, Examcollection Salesforce-AI-Specialist Free Dumps

Salesforce-AI-Specialist Exam Tips, Examcollection Salesforce-AI-Specialist Free Dumps

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Salesforce Certified AI Specialist Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?

  • A. Storing this data requires Data Cloud to be provisioned.
  • B. Storing this data requires Salesforce big objects.
  • C. Storing this data requires a custom object for data to be configured.

Answer: A

Explanation:
When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloud is a key consideration for storing and managing large-scale audit and feedback data. TheSalesforce Data Cloud(formerly known asCustomer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioningData Cloud, organizations likeUniversal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
* Audit and feedback datagenerated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and theData Cloudprovides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
* Custom objectsorSalesforce Big Objectsare not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions.Big Objectsare more suited for archival data, whereasData Cloudensures more robust processing, segmentation, and analysis capabilities.
References:
* Salesforce Data Cloud Documentation:https://www.salesforce.com/products/data-cloud/overview/
* Salesforce Einstein AI Overview:https://www.salesforce.com/products/einstein/overview/


NEW QUESTION # 71
What is the correct process to leverage Prompt Builder in a Salesforce org?

  • A. Select the appropriate prompt template type to use, develop the prompt within the prompt workspace, select resources to dynamically insert CRM-derived grounding data, pick the model to use, and test and validate the generated responses.
  • B. Select the appropriate prompt template type to use, select one of Salesforce's standard prompts, determine the object to associate the prompt, select a record to validate against, and associate the prompt to an action.
  • C. Enable the target object for generative prompting, develop the prompt within the prompt workspace, select records to fine-tune and ground the response, enable the Trust Layer, and associate the prompt to an action.

Answer: A

Explanation:
When using Prompt Builder in a Salesforce org, the correct process involves several important steps:
Select the appropriate prompt template type based on the use case.
Develop the prompt within the prompt workspace, where the template is created and customized.
Select CRM-derived grounding data to be dynamically inserted into the prompt, ensuring that the AI-generated responses are based on accurate and relevant data.
Pick the model to use for generating responses, either using Salesforce's built-in models or custom ones.
Test and validate the generated responses to ensure accuracy and effectiveness.
Option B is correct as it follows the proper steps for using Prompt Builder.
Option A and Option C do not capture the full process correctly.
Reference:
Salesforce Prompt Builder Documentation: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_overview.htm


NEW QUESTION # 72
An AI Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the AI Specialist recommend to achieve this use case?

  • A. Deploy UC's own custom foundational model on this data first.
  • B. Fine-Tune the standard foundational model due to the complexity of the data.
  • C. Use a prompt template grounded on CRH and Data Cloud data using standard foundation model.

Answer: C

Explanation:
For no-code implementations, Prompt Builder allows AI Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine- tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.


NEW QUESTION # 73
Universal Containers wants to be able to detect with a high level confidence if content generated by a large language model (LLM) contains toxic language.
Which action should an Al Specialist take in the Trust Layer to confirm toxicity is being appropriately managed?

  • A. Create a Trust Layer audit report within Data Cloud that uses a toxicity detector type filter to display toxic responses and their respective scores.
  • B. Create a flow that sends an email to a specified address each time the toxicity score from the response exceeds a predefined threshold.
  • C. Access the Toxicity Detection log in Setup and export all entries where isToxicityDetected is true.

Answer: A

Explanation:
To ensure that content generated by a large language model (LLM) is appropriately screened for toxic language, the AI Specialist should create a Trust Layer audit report within Data Cloud. By using the toxicity detector type filter, the report can display toxic responses along with their respective toxicity scores, allowing Universal Containers to monitor and manage any toxic content generated with a high level of confidence.
Option C is correct because it enables visibility into toxic language detection within the Trust Layer and allows for auditing responses for toxicity.
Option A suggests checking a toxicity detection log, but Salesforce provides more comprehensive options via the audit report.
Option B involves creating a flow, which is unnecessary for toxicity detection monitoring.
Reference:
Salesforce Trust Layer Documentation: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_audit.htm


NEW QUESTION # 74
An AI Specialist implements Einstein Sales Emails for a sales team. The team wants to send personalized follow-up emails to leads based on their interactions and data stored in Salesforce. The AI Specialist needs to configure the system to use the most accurate and up-to-date information for email generation.
Which grounding technique should the AI Specialist use?

  • A. Ground with Record Merge Fields
  • B. Automatic grounding using Draft with Einstein feature
  • C. Ground with Apex Merge Fields

Answer: B

Explanation:
For Einstein Sales Emails to generate personalized follow-up emails, it is crucial to ground the email content with the most up-to-date and accurate information. Grounding refers to connecting the AI model with real-time data. The most appropriate technique in this case is Ground with Record Merge Fields. This method ensures that the content in the emails pulls dynamic and accurate data directly from Salesforce records, such as lead or contact information, ensuring the follow-up is relevant and customized based on the specific record.
Record Merge Fields ensure the generated emails are highly personalized using data like lead name, company, or other Salesforce fields directly from the records.
Apex Merge Fields are typically more suited for advanced, custom logic-driven scenarios but are not the most straightforward for this use case.
Automatic grounding using Draft with Einstein is a different feature where Einstein automatically drafts the email, but it does not specifically ground the content with record-specific data like Record Merge Fields.
Reference:
Salesforce Einstein Sales Emails Documentation: https://help.salesforce.com/s/articleView?id=release-notes.rn_einstein_sales_emails.htm


NEW QUESTION # 75
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