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| Section | Weight | Objectives |
|---|---|---|
| Testing, Deployment & Lifecycle | 15% | - Agent lifecycle management
|
| AI Agents | 35% | - Agent behavior configuration
|
| Prompt Engineering | 20% | - Grounding techniques
|
| Governance & Observability | 10% | - AI governance
|
| Data Cloud & Grounding | 20% | - Data integration for AI agents
|
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NEW QUESTION # 126
Universal Containers (UC) has configured a data library and wants to restrict indexing of knowledge articles to articles which are only publicly available in their knowledge base, UC also wants the agent to link sources that the large language model (LLM) grounded its response on.
Which settings should help UC with this?
Answer: C
Explanation:
According to the AgentForce Data Library Configuration Guide, administrators can restrict indexing and retrieval of Knowledge articles to publicly available ones and enable source visibility for LLM-grounded responses. The documentation states:
"Within the data library settings, under Knowledge Settings, enable `Use Public Knowledge Articles' to ensure only publicly visible content is indexed. To display citations, enable `Show Sources' so the agent links the specific articles or data records used to ground its response."
NEW QUESTION # 127
Universal Containers implements three custom actions to get three distinct types of sales summaries for its users. Users are complaining that they are not getting the right summary based on their utterances. What should the Agentforce Specialist investigate as the root cause?
Answer: B
Explanation:
The root cause of users receiving incorrect sales summaries lies in non-unique action instructions (Option B). In Einstein Bots, custom actions are triggered based on how well user utterances align with the action instructions defined for each action. If the instructions for the three custom actions overlap or lack specificity, the bot's natural language processing (NLP) cannot reliably distinguish between them, leading to mismatched responses.
Steps to Investigate:
Review Action Instructions: Ensure each custom action has distinct, context-specific instructions.
For example:
Action 1: "Summarize quarterly sales by region."
Action 2: "Generate a product-wise sales breakdown for the current fiscal year." Action 3: "Provide a comparison of sales performance between online and in-store channels." Ambiguous or overlapping instructions (e.g., "Get sales summary") cause confusion.
Test Utterance Matching: Use Einstein Bot's training tools to validate if user utterances map to the correct action. Overlap indicates instruction ambiguity.
Refine Instructions: Incorporate keywords or phrases unique to each sales summary type to improve intent detection.
NEW QUESTION # 128
An Agentforce Specialist is building a Flex prompt template. Which best practice should the Agentforce Specialist follow when creating the Flex prompt template?
Answer: C
Explanation:
In the AgentForce Flex Prompt Design Guide, the recommended best practice for Flex prompt templates is to ensure that the large language model (LLM) is provided with clear contextual information and a defined role (e.g., sales rep, support agent, concierge). Flex prompts are intentionally open-ended and adaptable, enabling them to handle dynamic and conversational use cases, provided they are guided with sufficient context and role clarity.
By setting a role and including detailed contextual cues, the LLM can tailor tone, content, and reasoning appropriately while maintaining response accuracy.
NEW QUESTION # 129
Before activating a custom copilot action, An Agentforce would like is to understand multiple real- world user utterances to ensure the action being selected appropriately. Which tool should the Agentforce Specialist recommend?
Answer: B
Explanation:
Model Playground (specifically within the context of Generative AI and Copilot in Salesforce) allows you to test and refine the behavior of your AI models and, by extension, how your copilot actions interpret and respond to different user inputs (utterances). It's a sandbox environment where you can:
Input various user utterances.
See how the underlying Large Language Model (LLM) and the copilot's reasoning engine classify those utterances.
Observe which actions are triggered by those utterances.
Adjust the action instructions and examples to improve the copilot's understanding and ensure the correct action is chosen for specific user requests. This iterative testing is crucial for ensuring the action performs as expected in real-world scenarios.
NEW QUESTION # 130
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?
Answer: B
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 # 131
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