QUESTION: 5

Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should an AI Specialist recommend to resolve the issue?

Enable data masking for sandbox refreshes.
Configure data masking in the Einstein Trust Layer setup.
Add new data masking rules in LLM setup.

Answer(s): B

Explanation:

When Universal Containers' AI data masking rules do not meet organizational privacy and security standards, the AI Specialist should configure the data masking rules within the Einstein Trust Layer. The Einstein Trust Layer provides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations. Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data. Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through the Einstein Trust Layer, not the LLM configuration.

The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce AI Specialist

Reference:

For more information, refer to:
https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_data_masking.htm

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QUESTION: 6

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?

The records related to the prompt have not been selected.
The prompt has not been saved and activated,
A merge field has not been inserted in the prompt.

Answer(s): A

Explanation:

When the preview button is greyed out in 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

Reference:

Refer to the documentation on troubleshooting Flex templates here:
https://help.salesforce.com/s/articleView?id=sf.flex_prompt_builder_troubleshoot.htm

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QUESTION: 7

Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?

Model Builder
App Builder
Copilot Builder

Answer(s): A

Explanation:

To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model

Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities. Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
Salesforce AI Specialist

Reference:

For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm

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QUESTION: 8

An AI Specialist built a Field Generation prompt template that worked for many records, but users are reporting random failures with token limit errors.
What is the cause of the random nature of this error?

The number of tokens generated by the dynamic nature of the prompt template will vary by record.
The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
The number of tokens that can be processed by the LLM varies with total user demand.

Answer(s): A

Explanation:

The reason behind the token limit errors lies in the dynamic nature of the prompt template used in Field Generation. In Salesforce's AI generative models, each prompt and its corresponding output are subject to a token limit, which encompasses both the input and output of the large language model (LLM). Since the prompt template dynamically adjusts based on the specific data of each record, the number of tokens varies per record. Some records may generate longer outputs based on their data attributes, pushing the token count beyond the allowable limit for the LLM, resulting in token limit errors.
This behavior explains why users experience random failures--it is dependent on the specific data used in each case. For certain records, the combined input and output may fall within the token limit, while for others, it may exceed it. This variation is intrinsic to how dynamic templates interact with large language models.
Salesforce provides guidance in their documentation, stating that prompt template design should take into account token limits and suggests testing with varied records to avoid such random errors. It does not mention switching to Flex template type as a solution, nor does it suggest that token limits fluctuate with user demand. Token limits are a constant defined by the model itself, independent of external user load.

Reference:

Salesforce Developer Documentation on Token Limits for Generative AI Models Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)

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Free Salesforce-AI-Specialist Exam Braindumps (page: 2)

QUESTION: 5

Explanation:

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QUESTION: 6

Explanation:

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QUESTION: 7

Explanation:

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QUESTION: 8

Explanation:

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