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NEW QUESTION # 81
Universal Containers 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?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:In Salesforce Agentforce, prompt templates are used to generate dynamic responses or field values by leveraging an LLM, often with grounding data from Salesforce records or external sources. The scenario describes a Field Generation prompt template that fails intermittently with token limit errors, indicating that the issue is tied to exceeding the LLM's token capacity (e.g., input + output tokens). Therandom natureof these failures suggests variability in the token count across different records, which is directly addressed by Option B.
Prompt templates in Agentforce can be dynamic, meaning they pull in record-specific data (e.g., customer names, descriptions, or other fields) to generate output. Since the data varies by record-some records might have short text fields while others have lengthy ones-the total number of tokens (words, characters, or subword units processed by the LLM) fluctuates. When the token count exceeds the LLM's limit (e.g., 4,096 tokens for some models), the process fails, but this only happens for records with higher token-generating data, explaining the randomness.
* Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they're unrelated to token limits). This option is a distractor and not a verified solution.
* Option C: The LLM's token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.
References:
* Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help:https://help.salesforce.com
/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
* Trailhead: "Build Prompt Templates for Agentforce"(https://trailhead.salesforce.com/content/learn
/modules/build-prompt-templates-for-agentforce)
NEW QUESTION # 82
Universal Containers is evaluating Einstein Generative AI features to improve the productivity of the service center operation.
Which features should theAgentforce Specialistrecommend?
Answer: B
NEW QUESTION # 83
Universal Containers (UC) wants to use the Draft with Einstein feature in Sales Cloud to create a personalized introduction email.
After creating a proposed draft email, which predefined adjustment should UC choose to revise the draft with a more casual tone?
Answer: A
Explanation:
WhenUniversal Containersuses theDraft with Einsteinfeature inSales Cloudto create a personalized email, the predefined adjustment toMake Less Formalis the correct option to revise the draft with a more casual tone. This option adjusts the wording of the draft to sound less formal, making the communication more approachable while still maintaining professionalism.
* Enhance Friendlinesswould make the tone more positive, but not necessarily more casual.
* Optimize for Clarityfocuses on making the draft clearer but doesn't adjust the tone.
For more details, seeSalesforce documentation on Einstein-generated email draftsand tone adjustments.
NEW QUESTION # 84
Universal Containers' data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?
Answer: B
Explanation:
To accessexternally-hosted models, such as a large language model (LLM) hosted on AWS, theModel Builderin Salesforce is the appropriate tool.Model Builderallows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
* Option B, App Builder, is primarily used to build and configure applications in Salesforce, not to integrate AI models.
* Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation.
SalesforceAgentforce SpecialistReferences:For more details, check the Model Builder guide here:
https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
NEW QUESTION # 85
What is best practice when refining Agent custom action instructions?
Answer: C
Explanation:
When refining Agent custom action instructions, it is considered best practice to provide examples of user messages that 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 to Salesforce's Agent documentation on building and refining custom actions.
NEW QUESTION # 86
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