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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Einstein Trust Layer & Security | - Trust Layer & Security Features
| |
| Topic 2: Prompt Engineering | 30% | - Prompt Builder & Template Creation
|
| Topic 3: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 4: Agent Force Concepts | 30% | - Einstein GPT Integration
|
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NEW QUESTION # 216
An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the correct order. Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner service to work as expected?
Answer: A
Explanation:
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist must explicitly specify dependent actions using their API names in the custom action's configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A's API name must be listed as a dependency in Action B's instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.
NEW QUESTION # 217
An Agentforce Specialist is creating a custom action in Agentforce. Which option is available for the Agentforce Specialist to choose for the custom Agent action?
Answer: B
Explanation:
The Agentforce Specialist is defining a custom action for an Agentforce agent in Agent Builder.
Actions determine what the agent does (e.g., retrieve data, update records).
NEW QUESTION # 218
Universal Containers wants its AI agent to answer customer questions with precise and up-to- date information. How does an Agentforce Data Library simplify and enable this?
Answer: B
Explanation:
Why is "Automates Ingestion, Indexing, and Default Retriever Creation" the correct answer?
An Agentforce Data Library is a key component in ensuring that an AI agent provides precise and up-to-date responses by:
Automating data ingestion -> Brings in data from various sources.
Indexing the data -> Organizes it efficiently for AI retrieval.
Creating a default retriever -> Enables the AI to fetch relevant data dynamically when answering customer queries.
Key Features of an Agentforce Data Library:
Automates Data Ingestion
Integrates real-time and historical data into Salesforce Data Cloud.
Ensures that relevant updates are continuously fed into the AI system.
Indexes Data for Efficient Retrieval
Enhances searchability for quick, context-aware responses.
Enables fast AI response times while maintaining accuracy.
Creates a Default Retriever
AI agents use the retriever to fetch the most relevant and current information.
The retriever grounds AI-generated responses using structured and indexed data.
NEW QUESTION # 219
Universal Containers wants to incorporate CRM data as well-formatted JSON in a prompt to a large language model (LLM). What is an important consideration for this requirement?
Answer: A
Explanation:
Universal Containers (UC) wants to send well-formatted JSON data in a prompt to a large language model (LLM).
The question is about an important technical or design consideration for including CRM data as JSON in that prompt.
NEW QUESTION # 220
Universal Containers (UC) has registered an external service and created a template-triggered prompt flow that invokes the external service to fetch data from a REST API. UC now needs to make the response data from the external service usable inside a prompt template as a merge field when the template runs. How should UC meet this requirement?
Answer: C
Explanation:
As outlined in the AgentForce External Services and Prompt Flow Integration Guide, when data is retrieved from a registered external service via REST API, the response payload is stored as External Service Records. These records can then be referenced dynamically within prompt templates through External Service Record merge fields.
This approach allows the large language model (LLM) to use the fetched data as contextual grounding during prompt execution, ensuring that generated responses are accurate and consistent with the latest API results.
NEW QUESTION # 221
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