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NEW QUESTION # 69
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?
Answer: A
Explanation:
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.
Reference:
Salesforce Prompt Builder Permissions: https://help.salesforce.com/s/articleView?id=sf.prompt_builder_permissions.htm
NEW QUESTION # 70
A data science team has trained an XGBoost classification model for product recommendations onDatabricks.
The AI Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the AI Specialist set this up?
Answer: B
Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
* Create the Serving Endpoint in Databricks:
* The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
* Configure the Model Using Model Builder:
* After creating the endpoint, the AI Specialist should configure it within Einstein Studio'sModel Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
* Option B: Serving endpoints are not created in Einstein Studio; they are set up in external platforms like Databricks before integration.
* Option C: A Python SDK connector is not used to bring model inferences into Salesforce Data Cloud; Model Builder is the correct tool.
NEW QUESTION # 71
What is a Salesforce AI Specialist able to configure in Data Masking within the Einstein Trust Layer?
Answer: C
Explanation:
In the Einstein Trust Layer, the Salesforce AI Specialist can configure privacy data entities to be masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated when processed by AI models.
* Data Masking Configuration:
* The AI Specialist defines which fields or data types (e.g., email, phone number, Social Security Number) should be masked. For example, masking the Email field in a prompt response to protect user privacy.
* This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.
* Why Other Options Are Incorrect:
* A. Profiles exempt from masking: Exemptions are typically managed via permissions (e.g., field-level security), not directly within Einstein Trust Layer's Data Masking settings.
* B. Encryption keys for masking: Encryption is separate from masking. Masking involves obfuscation (e.g., replacing "john@example.com" with "@"), not encryption, which uses keys to secure data.
References:
* Einstein Trust Layer Documentation: States that Data Masking allows admins to "define which fields should be masked to protect sensitive data."
* Trailhead Module: "Einstein Trust Layer Basics" explains configuring privacy entities for masking.
* Salesforce Help Article: "Secure AI with Einstein Trust Layer" details masking configurations for privacy compliance.
NEW QUESTION # 72
What is the primary function of the planner service in the Einstein Copilot system?
Answer: C
Explanation:
The primary function of theplanner servicein theEinstein Copilotsystem is toidentify copilot actionsthat should be taken in response to user utterances. This service is responsible for analyzing the conversation and determining the appropriate actions (such as querying records, generating a response, or taking another action) that theEinstein Copilotshould perform based on user input.
NEW QUESTION # 73
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions.
Which use case functionality of Model Builder aligns with UC's request?
Answer: C
Explanation:
Customer churn predictionis the best use case forModel Builderin addressingUniversal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn,UCcan proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
* Option Bis correct becausecustomer churn predictionaligns withUC'sneed to reduce cancellations through proactive measures.
* Option A(product recommendation prediction) is unrelated to contract cancellations.
* Option C(contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.
References:
* Salesforce Model Builder Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
model_builder_use_cases.htm
NEW QUESTION # 74
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