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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Agent Force Concepts | 30% | - Data Cloud & Grounding Techniques
|
| Topic 2: Reasoning Engine & AI Actions | - Workflow mastery
| |
| Topic 3: Prompt Engineering | 30% | - Agentforce AI Setup & Configuration
|
| Topic 4: Einstein Trust Layer & Security | - Trust Layer & Security Features
|
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NEW QUESTION # 231
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: A
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). The random nature of 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.
NEW QUESTION # 232
Universal Containers (UC) is preparing to use the Agentforce Testing Center to ensure the reliability of a new agent. UC has a CSV file with test cases and is reviewing the documentation to understand best practices and limitations. Which best practice should the company follow to avoid modifying CRM data while running tests in the Testing Center?
Answer: B
Explanation:
According to the AgentForce Testing and Validation Guidelines, all automated or large-scale test runs in Testing Center should be executed in a sandbox environment to prevent any unintended modifications to live CRM data.
Running tests in production can trigger record updates, create cases, or call actions that alter live data, violating best practice standards for safe validation. Testing in a sandbox ensures the environment mirrors production logic while maintaining data isolation.
NEW QUESTION # 233
A data science team has trained an XGBoost classification model for product recommendations on Databricks. The Agentforce Specialist is tasked with bringing inferences for product recommendations from this model into Data Cloud as a stand-alone data model object (DMO).
How should the Agentforce Specialist set this up?
Answer: A
Explanation:
To integrate inferences from an XGBoost model into Salesforce's Data Cloud as a stand-alone Data Model Object (DMO):
Create the Serving Endpoint in Databricks:
The serving endpoint is necessary to make the trained model available for real-time inference.
Databricks provides tools to host and expose the model via an endpoint.
Configure the Model Using Model Builder:
After creating the endpoint, the Agentforce Specialist should configure it within Einstein Studio's Model Builder, which integrates external endpoints with Salesforce Data Cloud for processing and storing inferences as DMOs.
NEW QUESTION # 234
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: B
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 # 235
During configuration, Universal Containers (UC) forgot to grant Knowledge access to the Agentforce Service Agent. Which permission must UC add for the agent to interact with Knowledge articles and answer customer questions effectively?
Answer: C
Explanation:
According to the AgentForce for Service Configuration Guide, for an agent to interact with and retrieve Knowledge articles, it must have Knowledge object and field access, along with the
"Allow View Knowledge" permission. The documentation explains: "Agents need permission to access Knowledge records and their fields to retrieve and summarize content accurately.
Additionally, the Allow View Knowledge setting enables the agent to use the Knowledge object as a retrieval source."
NEW QUESTION # 236
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