SALESFORCE-AI-SPECIALIST NEW DUMPS EBOOK PASS CERTIFY | RELIABLE LATEST SALESFORCE-AI-SPECIALIST TEST QUESTIONS: SALESFORCE CERTIFIED AI SPECIALIST EXAM

Salesforce-AI-Specialist New Dumps Ebook Pass Certify | Reliable Latest Salesforce-AI-Specialist Test Questions: Salesforce Certified AI Specialist Exam

Salesforce-AI-Specialist New Dumps Ebook Pass Certify | Reliable Latest Salesforce-AI-Specialist Test Questions: Salesforce Certified AI Specialist Exam

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • Model Builder: This portion of the exam focuses on Salesforce AI specialists' expertise in working with AI models within Salesforce environments. Candidates will need to demonstrate knowledge of when to use the Model Builder and how to configure standard, custom, or Bring Your Own Large Language Model (BYOLLM) generative models to meet business needs.
Topic 2
  • Generative AI in CRM Applications: This part of the exam assesses AI specialists’ knowledge of generative AI within CRM systems. It covers the use of generative AI features in Einstein for Sales and Einstein for Service.
Topic 3
  • Prompt Builder: This section evaluates the expertise of AI specialists working with Salesforce's AI tools. It focuses on the Prompt Builder feature, requiring candidates to understand its usage based on business needs.
Topic 4
  • Agentforce Tools: In this topic, AI specialists get knowledge using agents when it is appropriate. Moreover, the topic explains the working of agents and reasoning engine powers Agentforce. Lastly, the topic focuses on managing and monitoring agent adoption.
Topic 5
  • Einstein Trust Layer: This section evaluates the skills of Salesforce AI specialists responsible for implementing security protocols and safeguarding data privacy. It emphasizes the security, privacy, and foundational features of the Einstein Trust Layer.

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Latest Salesforce-AI-Specialist Test Questions & Certification Salesforce-AI-Specialist Exam

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Salesforce Certified AI Specialist Exam Sample Questions (Q101-Q106):

NEW QUESTION # 101
Which feature in the Einstein Trust Layer helps to minimize the risks of jailbreaking and prompt injection attacks?

  • A. Data Masking
  • B. Prompt Defense
  • C. Secure Data Retrieval and Grounding

Answer: B

Explanation:
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.


NEW QUESTION # 102
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?

  • A. Create the serving endpoint in Databricks, then configure the model using a Python SDK connector.
  • B. Create the serving endpoint in Einstein Studio, then configure the model using Model Builder.
  • C. Create the serving endpoint in Databricks, then configure the model using Model Builder.

Answer: C

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 # 103
What should an AI Specialist consider when using related list merge fields in a prompt template associated with an Account object in Prompt Builder?

  • A. If person accounts have been enabled, merge fields will not be available for the Account object.
  • B. The Activities related list on the Account object is not supported because it is a polymorphic field.
  • C. Prompt generation will yield no response when there is no related list associated with an Account in runtime.

Answer: B

Explanation:
When using related list merge fields in a prompt template associated with the Account object inPrompt Builder, theActivities related listis not supported due to it being apolymorphic field. Polymorphic fields can reference multiple different types of objects, which makes them incompatible with some merge field operations in prompt generation.
* Option Bis incorrect because person accounts do not limit the availability of merge fields for the Account object.
* Option Cis irrelevant since even if no related lists are available at runtime, the prompt can still generate based on other available data fields.
For more information, refer toSalesforce documentationon supported fields and limitations inPrompt Builder.


NEW QUESTION # 104
Universal Containers (UC) is using Einstein Generative AI to generate an account summary. UC aims to ensure the content is safe and inclusive, utilizing the Einstein Trust Layer's toxicity scoring to assess the content's safety level.
What does a safety category score of 1 indicate in the Einstein Generative Toxicity Score?

  • A. Safe
  • B. Not safe
  • C. Moderately safe

Answer: A

Explanation:
In the Einstein Trust Layer, the toxicity scoring system is used to evaluate the safety level of content generated by AI, particularly to ensure that it is non-toxic, inclusive, and appropriate for business contexts. A toxicity score of 1 indicates that the content is deemed safe.
The scoring system ranges from 0 (unsafe) to 1 (safe), with intermediate values indicating varying degrees of safety. In this case, a score of 1 means that the generated content is fully safe and meets the trust and compliance guidelines set by the Einstein Trust Layer.
For further reference, check Salesforce's official Einstein Trust Layer documentation regarding toxicity scoring for AI-generated content.


NEW QUESTION # 105
What is best practice when refining Einstein Copilot custom action instructions?

  • A. Specify the persona who will request the action.
  • B. Use consistent introductory phrases and verbs across multiple action instructions.
  • C. Provide examples of user messages that are expected to trigger the action.

Answer: C

Explanation:
When refiningEinstein Copilot custom action instructions, it is considered best practice toprovide examples of user messagesthat are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.
* Option B(consistent phrases) can improve clarity but does not directly refine the triggering logic.
* Option C(specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.
For more details, refer toSalesforce's Einstein Copilot documentationon building and refining custom actions.


NEW QUESTION # 106
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