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| Section | Weight | Objectives |
|---|---|---|
| Multi-Agent Interoperability | 5% | - Model Context Protocol (MCP) - Agent API usage scenarios - Agent-to-agent communication |
| Development Lifecycle | 15% | - Testing agents in Testing Center - Deployment: sandbox to production - Version control and maintenance - Monitoring, adoption and optimization |
| Prompt Engineering | 20% | - Grounding techniques and best practices - Prompt template creation: field generation, flex types - Prompt Builder usage and use cases - Prompt management and user roles |
| Data Cloud for Agentforce | 20% | - Improving response accuracy with data grounding - Retrievers and search types: keyword, vector, hybrid - Unstructured data processing: chunking, indexing - Agentforce Data Library types |
| AI Agents | 35% | - Agent User security and permissions - Topics and actions configuration - Agent types: Service, Sales, Employee - Agent architecture and reasoning engine - Channel integration: digital experience, email, Slack - Deterministic controls: filters and variables |
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NEW QUESTION # 250
When using a prompt template, what should an Agentforce Specialist consider with their grounding data and chosen model?
Answer: C
Explanation:
The most critical technical consideration when pairing a prompt template's grounding data with a chosen Large Language Model (LLM) is the relationship between the two. The correct action is to review the model limitation in Prompt Builder versus the grounding data size (C).
Every LLM has a fixed context window limit, typically expressed in tokens (the model's units for processing text). This token limit defines the maximum amount of input data (the prompt template text + all the dynamic grounding data) and output data the model can handle in a single request.
The grounding data, which is pulled dynamically from Salesforce records (e.g., related lists, long text fields, Flow outputs), varies significantly in size from one record to the next. If the combined size of the prompt and the dynamic data for a specific record exceeds the LLM's token limit, the generative AI request will fail with a "token limit exceeded" error. The Agentforce Specialist must proactively design the template to limit the amount of data retrieved (e.g., using Flow to summarize related lists or querying only essential fields) to ensure it stays within the chosen model's capacity.
NEW QUESTION # 251
Universal Containers (UC) wants to ensure its compliance team can retrieve exact matches of policy clause numbers from a structured legal document library. Which search type should UC implement?
Answer: C
Explanation:
According to the AgentForce Search Optimization Guide, when the use case requires retrieving exact matches (such as policy clause numbers, legal identifiers, or invoice IDs) from structured data, the recommended approach is to use keyword search. The documentation specifies:
"Keyword search ensures deterministic retrieval of exact term matches from structured fields, preserving precision for identifiers, numeric values, and code references."
NEW QUESTION # 252
Universal Containers (UC) plans to answer questions based on similar cases that have been successfully resolved in the past. What should UC consider when implementing this approach?
Answer: A
Explanation:
According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.
The documentation clearly explains:
"When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases." This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user's query.
NEW QUESTION # 253
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: A
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 # 254
Universal Containers (UC) needs to improve the agent productivity in replying to customer chats.
Which generative AI feature should help UC address this issue?
Answer: C
Explanation:
Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.
Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.
Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.
Thus, Service Replies is the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.
NEW QUESTION # 255
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