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  2. Salesforce Certification
  3. Salesforce-AI-Specialist Exam
  4. Salesforce.Salesforce-AI-Specialist.v2024-12-05.q31 Dumps
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Question 21

An AI Specialist at Universal Containers (UC) Is tasked with creating a new custom prompt template to populate a field with generated output. UC enabled the Einstein Trust Layer to ensure AI Audit data is captured and monitored for adoption and possible enhancements.
Which prompt template type should the AI Specialist use and which consideration should they review?

Correct Answer: B
When creating acustom prompt templateto populate a field with generated output, the most appropriate template type isField Generation. This template is specifically designed for generating field-specific outputs using generative AI.
Additionally, the AI Specialist must ensure thatDynamic Fieldsare enabled.Dynamic Fieldsallow the system to use real-time data inputs from related records or fields when generating content, ensuring that the AI output is contextually accurate and relevant. This is crucial when populating specific fields with AI-generated content, as it ensures the data source remains dynamic and up-to-date.
TheEinstein Trust Layerwill track and audit the interactions to ensure the organization can monitor AI adoption and make necessary enhancements based on AI usage patterns.
For further reading, refer to Salesforce's guidelines onField Generation templatesand theEinstein Trust Layer.
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Question 22

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?

Correct Answer: A
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.
References:
* Salesforce Developer Documentation onToken Limits for Generative AI Models
* Salesforce AI Best Practices on Prompt Design (Trailhead or Salesforce blog resources)
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Question 23

An AI Specialist needs to create a Sales Email with a custom prompt template. They need to ground on the following data.
Opportunity Products Events near the customer Tone and voice examples
How should the AI Specialist obtain related items?

Correct Answer: A
To ground a sales email onOpportunity Products, Events near the customer, and Tone and voice examples, the AI Specialist should use aprompt-initiated flow. This flow can dynamically fetch the necessary data from related records in Salesforce and ground the generative AI output with contextually accurate information.
* Option B (flex template)does not provide the ability to fetch dynamic data from Salesforce records automatically.
* Option C (manual insertion)would not allow for the dynamic and automated grounding of data required for custom prompts.
Refer toSalesforce documentation on flowsand grounding for more details on integrating data into custom prompt templates.
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Question 24

Universal Containers Is Interested In Improving the sales operation efficiency by analyzing their data using Al-powered predictions in Einstein Studio.
Which use case works for this scenario?

Correct Answer: B
For improvingsales operations efficiency,Einstein Studiois ideal for creating AI-powered models that can predict outcomes based on data. One of the most valuable use cases is predictingcustomer lifetime value, which helps sales teams focus on high-value accounts and make more informed decisions.Customer lifetime value (CLV)predictions can optimize strategies around customer retention, cross-selling, and long-term engagement.
* Option Bis the correct choice as predicting customer lifetime value is a well-established use case for AI in sales.
* Option A(customer sentiment) is typically handled through NLP models, whileOption C(product popularity) is more of a marketing analysis use case.
References:
* Salesforce Einstein Studio Use Case Overview:https://help.salesforce.com/s/articleView?id=sf.
einstein_studio_overview
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Question 25

Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?

Correct Answer: C
Prompt Defenseis a feature in theEinstein Trust Layerthat helps minimize the risks ofjailbreakingand prompt injection attacks. These attacks occur when malicious users try to manipulate the AI model by providing unintended inputs.Prompt Defenseensures that the prompts are processed securely, protecting the system from such vulnerabilities.
* Option A(Secure Data Retrieval and Grounding) relates to ensuring that data used by AI is securely retrieved but does not address prompt security.
* Option B(Data Masking) focuses on protecting sensitive information but does not prevent injection attacks.
For more information, refer toSalesforce's Einstein Trust Layer documentationonPrompt Defenseand security features.
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