In Model Playground, which hyperparameters of an existing Salesforce-enabled foundational model can an AI Specialist change?
Correct Answer:
A
InModel 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 toSalesforce AI Model Playgroundguidance from Salesforce??s official documentation on foundational model adjustments.
What is the main purpose of Prompt Builder?
Correct Answer:
B
Prompt Builderis designed to help organizations create and configure reusable prompts for large language models (LLMs). By integratinggenerative AI responses into workflows,Prompt Builderenables customization of AI prompts that interact with Salesforce data and automate complex processes. This tool is especially useful for creating tailored and consistent AI-generated content in various business contexts, including customer service and sales.
✑ It is not a tool forApex programming(as in option A).
✑ It is also not limited to real-time suggestions as mentioned in option C. Instead, it provides a flexible way for companies to manage and customize how AI-driven responses are generated and used in their workflows.
References:
✑ Salesforce Prompt Builder
Overview:https://help.salesforce.com/s/articleView?id=sf.prompt_builder.htm
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?
Correct Answer:
C
Universal Containersshould use aPredictive modelto decide which of the three emails to send based on the customer'slifetime value scoreandmarket 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 provideexplainabilityto show why a certain email was chosen, which is crucial for UC??s requirement to explain the decision-making process.
✑ Generative modelsare typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
✑ Predictive modelsoffer the ability to explain why a particular decision was made,
which aligns with UC??s needs.
Refer toSalesforce??s Predictive AI model documentationfor more insights on how predictive models are used for segmentation and decision making.
Universal Containers (UC) recently rolled out Einstein Generative capabilities and has created a custom prompt to summarize case
records. Users have reported that the case summaries generated are not returning the appropriate information.
What is a possible explanation for the poor prompt performance?
Correct Answer:
A
Poor prompt performance when generating case summaries is often due to the data used forgroundingbeingincorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
✑ Option B(prompt template incompatibility with the LLM) is unlikely because such
incompatibility usually results in more technical failures, not poor content quality.
✑ Option C(Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer toSalesforce documentation on grounding AI modelsand data quality best practices.
An administrator wants to check the response of the Flex prompt template they've built, but the preview button is greyed out. What is the reason for this?
Correct Answer:
A
When thepreview button is greyed outin a Flex prompt template, it is often because the records related to the prompt have not been selected. Flex prompt templates pull data dynamically from Salesforce records, and if there are no records specified for the prompt, it can't be previewed since there is no content to generate based on the template.
✑ Option B, not saving or activating the prompt, would not necessarily cause the
preview button to be greyed out, but it could prevent proper functionality.
✑ Option C, missing a merge field, would cause issues with the output but would not directly grey out the preview button.
Ensuring that the related records are correctly linked is crucial for testing and previewing how the prompt will function in real use cases.
Salesforce AI Specialist References:Refer to the documentation on troubleshooting Flex templates
here:https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm
