At ValidExamDumps, we consistently monitor updates to the Salesforce-AI-Specialist exam questions by Salesforce. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Salesforce Certified AI Specialist exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Salesforce in their Salesforce-AI-Specialist exam. These outdated questions lead to customers failing their Salesforce Certified AI Specialist exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Salesforce-AI-Specialist exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
An AI Specialist turned on Einstein Generative AI in Setup. Now, the AI Specialist would like to create custom prompt templates in Prompt Builder. However, they cannot access Prompt Builder in the Setup menu.
What is causing the problem?
In order to access and create custom prompt templates in Prompt Builder, the AI Specialist must have the Prompt Template Manager permission set assigned. Without this permission, they will not be able to access Prompt Builder in the Setup menu, even though Einstein Generative AI is enabled.
Option B is correct because the Prompt Template Manager permission set is required to use Prompt Builder.
Option A (Prompt Template User permission set) is incorrect because this permission allows users to use prompts, but not create or manage them.
Option C (LLM configuration in Data Cloud) is unrelated to the ability to access Prompt Builder.
In Model Playground, which hyperparameters of an existing
Salesforce-enabled foundational model can an AI Specialist change?
In Model Playground, an AI specialist working with a Salesforce-enabled foundational model has control over specific hyperparameters that can directly affect the behavior of the generative model:
Temperature: Controls the randomness of predictions. A higher temperature leads to more diverse outputs, while a lower temperature makes the model's responses more focused and deterministic.
Frequency Penalty: Reduces the likelihood of the model repeating the same phrases or outputs frequently.
Presence Penalty: Encourages the model to introduce new topics in its responses, rather than sticking with familiar, previously mentioned content.
These hyperparameters are adjustable to fine-tune the model's responses, ensuring that it meets the desired behavior and use case requirements. Salesforce documentation confirms that these three are the key tunable hyperparameters in the Model Playground.
For more details, refer to Salesforce AI Model Playground guidance from Salesforce's official documentation on foundational model adjustments.
Universal Containers (UC) wants to enable its sales team to use Al to suggest recommended products from its catalog.
Which type of prompt template should UC use?
Universal Containers (UC) wants to enable its sales team to leverage AI to recommend products from its catalog. The best option for this use case is a Flex prompt template.
A Flex prompt template is designed to provide flexible, customizable AI-driven recommendations or responses based on specific data points, such as product information, customer needs, or sales history. This template type allows the AI to consider various inputs and parameters, making it ideal for generating product recommendations dynamically.
In contrast:
A Record summary prompt template (Option A) is used to summarize data related to a specific record, such as generating a quick summary of a sales opportunity or account, but not for recommending products.
An Email generation prompt template (Option B) is tailored for crafting email content and is not suitable for suggesting products based on a catalog.
Given the need for dynamic recommendations that pull from a product catalog and potentially other sales data, the Flex prompt template is the correct approach.
Salesforce Reference:
Salesforce Prompt Templates Overview: https://help.salesforce.com/s/articleView?id=000391407&type=1
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?
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 Reference:
Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1
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 the AI Specialist highlight to UC that addresses these requirements?
Universal Containers (UC) has concerns about data privacy when using Salesforce's generative AI features, particularly around preventing third-party LLMs from accessing or retaining their data. The Zero-Data Retention Policy in the Einstein Trust Layer is designed to address these concerns by ensuring that:
No data is used for training or product improvements by third-party LLMs.
No data is retained outside 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 Defense and Data Masking are also security features, but they do not directly address the concerns related to third-party data access and retention.
