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Salesforce Salesforce-AI-Specialist Useful Dumps | Vce Salesforce-AI-Specialist Torrent

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Salesforce Salesforce-AI-Specialist Exam Syllabus Topics:

Topic
Details

Topic 1

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 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

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.

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Salesforce Certified AI Specialist Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
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?

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

Answer: B

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)

NEW QUESTION # 109
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system.
Which data grounding technique should the AI Specialist recommend?

A. Apex Merge Fields
B. Eternal Object Record Merge Fields
C. External Services Merge Fields

Answer: B

Explanation:
* Context of the Requirement:Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
* Data Grounding in LLM Prompts:Data grounding ensures the Large Language Model has access to the most current and relevant information. In Salesforce, one recommended approach is to useExternal Objects(via Salesforce Connect) when data resides outside of Salesforce.
* Why External Object Record Merge Fields:
* External Objectsappear much like standard or custom objects but map to tables in external systems.
* You can reference fields from these External Objects in merge fields, allowing real-time data retrieval from the external ERP system without storing that data natively in Salesforce.
* This is a simpler "point-and-reference" approach compared to coding custom Apex or configuring external services for direct prompt embedding.
* Why Not External Services Merge Fields or Apex Merge Fields:
* External Services Merge Fieldstypically leverage flows or external service definitions. While feasible, it is more about orchestrating or invoking external services for automation (e.g., Flow).
It's not the standard approach for seamlessly referencingexternal recorddata in prompt merges.
* Apex Merge Fieldswould imply custom Apex code controlling the prompt insertion. While possible, it's less "clicks not code" friendly and is not the default method for referencing typical record data.
* References and Study Resources:
* Salesforce Help & Training#Salesforce Connect and External Objects
* Salesforce Trailhead#"Integrate External Data with Salesforce Connect"
* Salesforce AI Specialist Study Resources(documentation regarding how to ground LLM prompts using External Objects)

NEW QUESTION # 110
Universal Containers (UC) recently rolled out Einstein Generative capabilities and has created a custom prompt to summarize case records. Users have reported that the case summaries generated are not returning the appropriate information.
What is a possible explanation for the poor prompt performance?

A. The Einstein Trust Layer is incorrectly configured.
B. The prompt template version is incompatible with the chosen LLM.
C. The data being used for grounding Is incorrect or incomplete.

Answer: C

Explanation:
Poor prompt performance when generating case summaries is often due to the data used forgroundingbeing incorrect or incomplete. Grounding involves feeding accurate, relevant data to the AI so it can generate appropriate outputs. If the data source is incomplete or contains errors, the generated summaries will reflect that by being inaccurate or insufficient.
* Option B(prompt template incompatibility with the LLM) is unlikely because such incompatibility usually results in more technical failures, not poor content quality.
* Option C(Einstein Trust Layer misconfiguration) is focused on data security and auditing, not the quality of prompt responses.
For more information, refer toSalesforce documentation on grounding AI modelsand data quality best practices.

NEW QUESTION # 111
What is the role of the large language model (LLM) in executing an Einstein Copilot Action?

A. Determine a user's access and sort actions by priority to be executed
B. Find similar requests and provideactions that need to be executed
C. Identify the best matching actions and correct order of execution

Answer: C

Explanation:
In Einstein Copilot, the role of the Large Language Model (LLM) is to analyze user inputs and identify the best matching actions that need to be executed. It uses natural language understanding to break down the user' s request and determine the correct sequence of actions that should be performed.
By doing so, the LLM ensures that the tasks and actions executed are contextually relevant and are performed in the proper order. This process provides a seamless, AI-enhanced experience for users by matching their requests to predefined Salesforce actions or flows.
The other options are incorrect because:
A mentions finding similar requests, which is not the primary role of the LLM in this context.
C focuses on access and sorting by priority, which is handled more by security models and governance than by the LLM.
References:
Salesforce Einstein Documentation on Einstein Copilot Actions
Salesforce AI Documentation on Large Language Models

NEW QUESTION # 112
Universal Containers (UC) is implementing Einstein Generative AI to improve customer insights and interactions. UC needs audit and feedback data to be accessible for reporting purposes.
What is a consideration for this requirement?

A. Storing this data requires Data Cloud to be provisioned.
B. Storing this data requires a custom object for data to be configured.
C. Storing this data requires Salesforce big objects.

Answer: A

Explanation:
When implementingEinstein Generative AIfor improved customer insights and interactions, theData Cloud is a key consideration for storing and managing large-scale audit and feedback data. TheSalesforce Data Cloud(formerly known asCustomer 360 Audiences) is designed to handle and unify massive datasets from various sources, making it ideal for storing data required for AI-powered insights and reporting. By provisioningData Cloud, organizations likeUniversal Containers (UC)can gain real-time access to customer data, making it a central repository for unified reporting across various systems.
* Audit and feedback datagenerated by Einstein Generative AI needs to be stored in a scalable and accessible environment, and theData Cloudprovides this capability, ensuring that data can be easily accessed for reporting, analytics, and further model improvement.
* Custom objectsorSalesforce Big Objectsare not designed for the scale or the specific type of real- time, unified data processing required in such AI-driven interactions.Big Objectsare more suited for archival data, whereasData Cloudensures more robust processing, segmentation, and analysis capabilities.
References:
* Salesforce Data Cloud Documentation:https://www.salesforce.com/products/data-cloud/overview/
* Salesforce Einstein AI Overview:https://www.salesforce.com/products/einstein/overview/

NEW QUESTION # 113
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