AI-201합격보장가능시험덤프 - AI-201인기자격증인증시험덤프

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Salesforce AI-201 Exam Syllabus Topics:

Section	Weight	Objectives
Agent Force Concepts	30%	- Einstein GPT Integration 1. Advanced implementation strategies - Data Cloud & Grounding Techniques 1. Real-world applications
Prompt Engineering	30%	- Agentforce AI Setup & Configuration 1. Advanced implementation strategies - Prompt Builder & Template Creation 1. Best practices
Einstein Trust Layer & Security		- Trust Layer & Security Features 1. Enterprise-grade deployment - Data Masking 1. Configuration of privacy data entities
Reasoning Engine & AI Actions		- Workflow mastery 1. Custom Agent actions (Apex) 2. Custom Agent actions (Flows) 3. Standard Agent actions

>> AI-201합격보장 가능 시험덤프 <<

AI-201인기자격증 인증시험덤프 & AI-201덤프최신버전

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최신 AI Specialist AI-201 무료샘플문제 (Q88-Q93):

질문 # 88
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?

A. The number of tokens generated by the dynamic nature of the prompt template will vary by record.
B. The template type needs to be switched to Flex to accommodate the variable amount of tokens generated by the prompt grounding.
C. The number of tokens that can be processed by the LLM varies with total user demand.

정답：A

설명：
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.

질문 # 89
How does the AI Retriever function within Data Cloud?

A. It automatically extracts and reformats raw data from diverse sources into standardized datasets for use in historical trend analysis and forecasting.
B. It monitors and aggregates data quality metrics across various data pipelines to ensure only high- integrity data is used for strategic decision-making.
C. It performs contextual searches over an indexed repository to quickly fetch the most relevant documents, enabling grounding AI responses with trustworthy, verifiable information.

정답：C

설명：
The AI Retriever is a key component in Salesforce Data Cloud, designed to support AI-driven processes like Agentforce by retrieving relevant data. Let's evaluate each option based on its documented functionality.

질문 # 90
Universal Containers has a strict change management process that requires all possible configuration to be completed in a sandbox which will be deployed to production. The Agentforce Specialist is tasked with setting up Work Summaries for Enhanced Messaging. Einstein Generative AI is already enabled in production, and the Einstein Work Summaries permission set is already available in production. Which other configuration steps should the Agentforce Specialist take in the sandbox that can be deployed to the production org?

A. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: and ado the Wrap up component to the Messaging session record page lavcut.
B. From the Epstein setup menu, select Turn on Einstein: create custom fields to store Issue, Resolution, and Summary: create a Quick Action that updates these fields: and add the wrap up componert to the Messaging session record page layout.
C. Create custom fields to store issue, Resolution, and Summary; create a Quick Action that updates these fields: add the Wrap Up component to the Messaging Session record paae layout: and create Permission Set Assignments for the intended Agents.

정답：A

설명：
Universal Containers (UC) has a strict change management process that requires all possible configuration be completed in a sandbox and deployed to Production.
Einstein Generative AI is already enabled in Production, and the "Einstein Work Summaries" permission set is already available in Production.
The Agentforce Specialist needs to configure Work Summaries for Enhanced Messaging in the sandbox.
What Can Actually Be Deployed from Sandbox to Production?
Custom Fields: Metadata that is easily created in sandbox and then deployed.
Quick Actions: Also metadata-based and can be deployed from sandbox to production.
Layout Components: Page layout changes (such as adding the Wrap Up component) can be added to a change set or deployment package.

질문 # 91
An Agentforce is tasked with analyzing Agent interactions looking into user inputs, requests, and queries to identify patterns and trends. What functionality allows the AX Specialist to achieve this?

A. User Utterances dashboard
B. Agent Event Logs dashboard
C. AI Audit & Feedback Data dashboard

정답：A

설명：
The User Utterances dashboard (Option A) is the correct functionality for analyzing user inputs, requests, and queries to identify patterns and trends. This dashboard aggregates and categorizes the natural language inputs (utterances) from users, enabling the Agentforce Specialist to:
Identify Common Queries: Surface frequently asked questions or recurring issues.
Detect Intent Patterns: Understand how users phrase requests, which helps refine intent detection models.
Improve Bot Training: Highlight gaps in training data or misclassified utterances that require adjustment.

질문 # 92
Universal Containers (UC) wants to enable its sales reps to explore opportunities that are similar to previously won opportunities by entering the utterance, "Show me other opportunities like this one." How should UC achieve this with Agents?

A. Use the standard Agent action.
B. Create a custom Agent action calling an Apex class.
C. Create a custom Agent action calling a flow.

정답：A

설명：
Universal Containers can achieve the request to explore similar opportunities by using the standard Copilot action. Agent has built-in actions to handle natural language queries, such as
"Show me other opportunities like this one." The standard action will process the query and return results based on predefined matching criteria like opportunity details and past Closed Won deals.
This approach avoids the need to create custom flows or Apex classes, leveraging out-of-the-box functionality.

질문 # 93
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