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| Section | Weight | Objectives |
|---|---|---|
| Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Agent Force Concepts | 30% | - Einstein GPT Integration
|
| Einstein Trust Layer & Security | - Data Masking
| |
| Reasoning Engine & AI Actions | - Workflow mastery
|
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NEW QUESTION # 201
What is the purpose of applying filters in a custom retriever configuration?
Answer: A
Explanation:
The AgentForce Retriever Configuration Guide specifies that filters are used to refine and constrain search results within a retriever setup. Filters operate by applying conditions (up to 10) on indexed fields such as document type, category, region, or update date. This targeted filtering ensures that the retrieved data is highly relevant to the current user query or context.
For instance, an AgentForce retriever could be configured to include only documents tagged as
"Active" or within a specific product line, reducing noise and improving grounding accuracy for the LLM. This mechanism supports precision retrieval, which directly improves both the accuracy and reliability of generated responses.
NEW QUESTION # 202
Universal Containers wants to leverage the Record Snapshots grounding feature in a prompt template. What preparations are required?
Answer: B
Explanation:
Record Snapshots in Salesforce Prompt Builder leverage the data visible on the user's page layout for an object to ground the prompt. This means that the fields and related lists that are configured on the page layout directly influence the data included in the snapshot.
NEW QUESTION # 203
An agent incorrectly updates records when given ambiguous user instructions. What is the BEST mitigation strategy?
Answer: A
Explanation:
Clear prompt instructions, guardrails, and constraints reduce unintended actions caused by ambiguity.
NEW QUESTION # 204
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature. What does Einstein consider when generating a summary?
Answer: A
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
NEW QUESTION # 205
Universal Containers (UC) plans to send one of three different emails to its customers based on the customer's lifetime value score and their market segment. Considering that UC are required to explain why an e-mail was selected, which AI model should UC use to achieve this?
Answer: A
Explanation:
Universal Containers should use a Predictive model to decide which of the three emails to send based on the customer's lifetime value score and market segment. Predictive models analyze data to forecast outcomes, and in this case, it would predict the most appropriate email to send based on customer attributes. Additionally, predictive models can provide explainability to show why a certain email was chosen, which is crucial for UC's requirement to explain the decision- making process.
Generative models are typically used for content creation, not decision-making, and thus wouldn't be suitable for this requirement.
Predictive models offer the ability to explain why a particular decision was made, which aligns with UC's needs.
NEW QUESTION # 206
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