FREE PDF QUIZ UNPARALLELED SALESFORCE - SALESFORCE-AI-SPECIALIST - TEST SALESFORCE CERTIFIED AI SPECIALIST EXAM PREPARATION

Free PDF Quiz Unparalleled Salesforce - Salesforce-AI-Specialist - Test Salesforce Certified AI Specialist Exam Preparation

Free PDF Quiz Unparalleled Salesforce - Salesforce-AI-Specialist - Test Salesforce Certified AI Specialist Exam Preparation

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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 4
  • 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.
Topic 5
  • 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.

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Salesforce Certified AI Specialist Exam Sample Questions (Q111-Q116):

NEW QUESTION # 111
How does the Einstein Trust Layer ensure that sensitive data isprotected while generating useful and meaningful responses?

  • A. Responses that do not meet the relevance threshold will be automatically rejected.
  • B. Masked data will be de-masked during response journey.
  • C. Masked data will be de-masked during request journey.

Answer: B

Explanation:
The Einstein Trust Layer ensures that sensitive data is protected while generating useful and meaningful responses by masking sensitive data before it is sent to the Large Language Model (LLM) and then de- masking it during the response journey.
How It Works:
* Data Masking in the Request Journey:
* Sensitive Data Identification:Before sending the prompt to the LLM, the Einstein Trust Layer scans the input for sensitive data, such as personally identifiable information (PII), confidential business information, or any other data deemed sensitive.
* Masking Sensitive Data:Identified sensitive data is replaced with placeholders or masks. This ensures that the LLM does not receive any raw sensitive information, thereby protecting it from potential exposure.
* Processing by the LLM:
* Masked Input:The LLM processes the masked prompt and generates a response based on the masked data.
* No Exposure of Sensitive Data:Since the LLM never receives the actual sensitive data, there is no risk of it inadvertently including that data in its output.
* De-masking in the Response Journey:
* Re-insertion of Sensitive Data:After the LLM generates a response, the Einstein Trust Layer replaces the placeholders in the response with the original sensitive data.
* Providing Meaningful Responses:This de-masking process ensures that the final response is both meaningful and complete, including the necessary sensitive information where appropriate.
* Maintaining Data Security:At no point is the sensitive data exposed to the LLM or any unintended recipients, maintaining data security and compliance.
Why Option A is Correct:
* De-masking During Response Journey:The de-masking process occurs after the LLM has generated its response, ensuring that sensitive data is only reintroduced into the output at the final stage, securely and appropriately.
* Balancing Security and Utility:This approach allows the system to generate useful and meaningful responses that include necessary sensitive information without compromising data security.
Why Options B and C are Incorrect:
* Option B (Masked data will be de-masked during request journey):
* Incorrect Process:De-masking during the request journey would expose sensitive data before it reaches the LLM, defeating the purpose of masking and compromising data security.
* Option C (Responses that do not meet the relevance threshold will be automatically rejected):
* Irrelevant to Data Protection:While the Einstein Trust Layer does enforce relevance thresholds to filter out inappropriate or irrelevant responses, this mechanism does not directly relate to the protection of sensitive data. It addresses response quality rather than data security.
References:
* Salesforce AI Specialist Documentation -Einstein Trust Layer Overview:
* Explains how the Trust Layer masks sensitive data in prompts and re-inserts it after LLM processing to protect data privacy.
* Salesforce Help -Data Masking and De-masking Process:
* Details the masking of sensitive data before sending to the LLM and the de-masking process during the response journey.
* Salesforce AI Specialist Exam Guide -Security and Compliance in AI:
* Outlines the importance of data protection mechanisms like the Einstein Trust Layer in AI implementations.
Conclusion:
The Einstein Trust Layer ensures sensitive data is protected by masking it before sending any prompts to the LLM and then de-masking it during the response journey. This process allows Salesforce to generate useful and meaningful responses that include necessary sensitive information without exposing that data during the AI processing, thereby maintaining data security and compliance.


NEW QUESTION # 112
An Al Specialist is creating a custom action for Agentforce.
Which setting should the AI Specialist test and iterate on to ensure the action performs as expected?

  • A. Action Instructions
  • B. Action Name
  • C. Action Input

Answer: A

Explanation:
To ensure a custom action in Agentforce performs as expected, the AI Specialist must focus on Action Instructions. Here's why:
* Action Instructions define the logic, parameters, and steps the AI should follow to execute the action.
They include:
* How input data is processed.
* API calls or Apex invocations.
* Conditional logic (e.g., decision trees).Testing and iterating on these instructions ensures alignment with the intended workflow. For example, incorrect API endpoint references or misconfigured parameters in the instructions will cause failures.
* Action Input (Option A) refers to the data provided to the action. While validating input formats is important, inputs are static once defined. The primary issue lies in whether the instructions correctly use the inputs.
* Action Name (Option B) is a descriptive label and does not affect functionality.
Salesforce Documentation Support:
* Salesforce Einstein Bots & Custom Actions Guide highlights that Action Instructions are where the
"core logic" resides, requiring rigorous testing (Source: Einstein Bots Developer Guide).
* Trailhead Module "Build Custom Actions for Einstein Bots" emphasizes refining instructions to handle edge cases and validate outputs (Source: Trailhead).
By iterating on Action Instructions, the AI Specialist ensures the action's logic, integrations, and error handling are robust.


NEW QUESTION # 113
Universal Container (UC) has effectively utilized prompt templates to update summary fields on Lightning record pages. An admin now wishes to incorporate similar functionality into UC's automation process using Flow.
How can the admin get a response from this prompt template from within a flow to use as part of UC's automation?

  • A. Invocable Apex
  • B. Flow Action
  • C. Einstein for Flow

Answer: C

Explanation:
* Context of the Question
* Universal Container (UC) has used prompt templates to update summary fields on record pages.
* Now, the admin wants to incorporate similar generative AI functionality within a Flow for automation purposes.
* How to Call a Prompt Template Within a Flow
* Flow Action: Salesforce provides a standard way to invoke generative AI templates or prompts within a Flow step. From the Flow Builder, you can add an "Action" that references the prompt template you created in Prompt Builder.
* Other Options:
* Invocable Apex: Possible fallback if there's no out-of-the-box Flow Action available.
However, Salesforce is releasing native Flow integration for AI prompts, making custom Apex less necessary.
* Einstein for Flow: A broad label for Salesforce's generative AI features within Flow.
Under the hood, you typically use a "Flow Action" that points to your prompt.
* Conclusion
* The easiest out-of-the-box solution is to use a Flow Action referencing the prompt template.
Hence, Option B is correct.
Salesforce AI Specialist References & Documents
* Salesforce Trailhead: Use Prompt Templates in FlowDemonstrates how to add an Action in Flow that calls a prompt template.
* Salesforce Documentation: Einstein GPT for FlowExplains standard flow actions to invoke and handle generative AI responses.


NEW QUESTION # 114
After creating a foundation model in Einstein Studio, which hyperparameter should an AI Specialist use to adjust the balance between consistency and randomness of a response?

  • A. Presence Penally
  • B. Temperature
  • C. Variability

Answer: B

Explanation:
The Temperature hyperparameter controls the randomness of model outputs:
* Low Temperature (e.g., 0.2): More deterministic, consistent responses.
* High Temperature (e.g., 1.0): More creative, varied responses.
* Presence Penalty (Option A): Discourages repetition of tokens, unrelated to randomness.
* Variability (Option B): Not a standard hyperparameter in Einstein Studio.
References:
* Einstein Studio Documentation: Model Hyperparameters
* Explicitly states "Temperature adjusts the balance between predictable and random outputs."


NEW QUESTION # 115
Northern Trail Outfitters (NTO) wants to configure Einstein Trust Layer in its production org but is unable to see the option on the Setup page.
After provisioning Data Cloud, which step must an Al Specialist take to make this option available to NTO?

  • A. Turn on Prompt Builder.
  • B. Turn on Einstein Copilot.
  • C. Turn on Einstein Generative AI.

Answer: C

Explanation:
For Northern Trail Outfitters (NTO) to configure the Einstein Trust Layer, the Einstein Generative AI feature must be enabled. The Einstein Trust Layer is closely tied to generative AI capabilities, ensuring that AI-generated content complies with data privacy, security, and trust standards.
Option A (Turning on Einstein Copilot) is unrelated to the setup of the Einstein Trust Layer, which focuses more on generative AI interactions and data handling.
Option C (Turning on Prompt Builder) is used for configuring and building AI-driven prompts, but it does not enable the Einstein Trust Layer.
Salesforce AI Specialist Reference:
For more details on the Einstein Trust Layer and setup steps: https://help.salesforce.com/s/articleView?id=sf.einstein_trust_layer_overview.htm


NEW QUESTION # 116
......

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