AI-201 Fragen&Antworten, AI-201 Deutsch Prüfungsfragen

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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Testing, Deployment & Lifecycle	15%	- Agent lifecycle management 1. Sandbox to production deployment 2. Testing and validation strategies
Data Cloud & Grounding	20%	- Data integration for AI agents 1. Connecting external data sources 2. Using Data Cloud for retrieval augmentation
Governance & Observability	10%	- AI governance 1. Trust layer and compliance concepts 2. Monitoring agent behavior
AI Agents	35%	- Agent behavior configuration 1. Filters and variables for deterministic behavior 2. Agent reasoning engine concepts - Agent setup and orchestration 1. Standard and custom agent actions 2. Standard and custom topics
Prompt Engineering	20%	- Prompt Builder usage 1. Field generation and flex templates 2. Prompt templates and configuration - Grounding techniques 1. Data grounding for reducing hallucinations 2. Best practices for prompt design

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Salesforce Certified Agentforce Specialist AI-201 Prüfungsfragen mit Lösungen (Q288-Q293):

288. Frage
Universal Containers (UC) is rolling out an AI-powered support assistant to help customer service agents quickly retrieve relevant troubleshooting steps and policy guidelines. The assistant relies on a search index in Data Cloud that contains product manuals, policy documents, and past case resolutions. During testing, UC notices that agents are receiving too many irrelevant results from older product versions that no longer apply. How should UC address this issue?

A. Modify the search index to only store documents from the last year and remove older records.
B. Use the default retriever, as it already searches the entire search index and provides broad coverage.
C. Create a custom retriever in Einstein Studio, and apply filters for publication date and product line.

Antwort: B

Begründung:
UC's support assistant uses a Data Cloud search index for grounding, but irrelevant results from outdated product versions are an issue.

289. Frage
An Agentforce is creating a custom action for Agentforce. Which setting should the a ensure the action performs as expected?

A. Action Instructions
B. Action Name
C. Action Input

Antwort: A

Begründung:
When creating a custom action for Einstein Bots in Salesforce (including Agentforce), Action Instructions are critical for defining how the bot processes and executes the action. These instructions guide the bot on the logic to follow, such as API calls, data transformations, or conditional steps. Testing and iterating on the instructions ensures the bot understands how to handle dynamic inputs, external integrations, and decision-making.
Salesforce documentation emphasizes that Action Instructions directly impact the bot's ability to execute workflows accurately. For example, poorly defined instructions may lead to incorrect API payloads or failure to parse responses. The Einstein Bot Developer Guide highlights that refining instructions is essential for aligning the bot's behavior with business requirements.

290. Frage
Universal Container's internal auditing team asks An Agentforce to verify that address information is properly masked in the prompt being generated. How should the Agentforce Specialist verify the privacy of the masked data in the Einstein Trust Layer?

A. Inspect the AI audit trail
B. Enable data encryption on the address field
C. Review the platform event logs

Antwort: A

Begründung:
The AI audit trail in Salesforce provides a detailed log of AI activities, including the data used, its handling, and masking procedures applied in the Einstein Trust Layer. It allows the Agentforce Specialist to inspect and verify that sensitive data, such as addresses, is appropriately masked before being used in prompts or outputs.
Enable data encryption on the address field: While encryption ensures data security at rest or in transit, it does not verify masking in AI operations.
Review the platform event logs: Platform event logs capture system events but do not specifically focus on the handling or masking of sensitive data in AI processes.
Inspect the AI audit trail: This is the most relevant option, as it provides visibility into how data is processed and masked in AI activities.

291. Frage
What is the role of the large language model (LLM) in understanding intent and executing an Agent Action?

A. Identify the best matching topic and actions and correct order of execution.
B. Determine a user's topic access and sort actions by priority to be executed.
C. Find similar requested topics and provide the actions that need to be executed.

Antwort: A

Begründung:
In Agentforce, the large language model (LLM), powered by the Atlas Reasoning Engine, interprets user requests and drives Agent Actions.

292. Frage
An Agentforce agent must retrieve data from Salesforce and an external system. What is the recommended approach?

A. Disable grounding
B. Train the LLM on external data
C. Copy external data into prompts manually
D. Use Apex or Flow with external integrations

Antwort: D

Begründung:
External data access should be handled via Apex or Flow integrations, not direct LLM training.

293. Frage
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