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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Exam Format: | Multiple Select, Scenario-based Questions, Multiple Choice |
| Exam Duration: | 105 minutes |
| Exam Price: | USD 200 |
| Passing Score: | 72%-73% |
| Real Exam Qty: | 60 multiple-choice questions + up to 5 unscored questions |
| Related Certifications: | Salesforce Platform App Builder Salesforce AI Specialist (retired) Salesforce AI Associate (retired) Salesforce Platform Administrator |
| Available Languages: | Japanese, French, English |
| Recommended Training: | Salesforce Trailhead Agentforce Learning Paths Agentforce Specialist Exam Prep |
| Exam Registration: | Certification Exam Guide Salesforce Certification Registration |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite testing center |
| Pre Condition: | None |
| Official Syllabus URL: | https://help.salesforce.com/s/articleView?id=005298924&type=1 |
>> Agentforce-Specialist Certification Questions <<
After cracking the Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) exam you will receive the credential badge. It will pave your way toward well-paying jobs or promotions in any reputed tech company. At TopExamCollection have customizable Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) practice exams for the students to review and improve their preparation. The Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) practice test material product of TopExamCollection are created by experts with the dedication to help customers crack the Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) exam on the first attempt.
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NEW QUESTION # 323
Universal Containers (UC) wants to limit an agent's access to Knowledge articles while deploying the
"Answer Questions with Knowledge" action. How should UC achieve this?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
UC wants to restrict the "Answer Questions with Knowledge" action to a subset of Knowledge articles. Let's evaluate the options for scoping agent access.
* Option A: Define scope instructions to the agent specifying a list of allowed article titles or IDs.
Agent instructions in Agent Builder guide behavior but cannot enforce granular data access restrictions like a specific list of article titles or IDs. This approach is impractical and bypasses Salesforce's security model, making it incorrect.
* Option B: Update the Data Library Retriever to filter on a custom field on the Knowledge article.
While Data Library Retrievers in Data Cloud can filter data, this requires custom development (e.g., modifying indexing logic) and assumes articles are ingested with a custom field for filtering. This is less straightforward than native Knowledge features and not a standard option, making it incorrect.
* Option C: Assign Data Categories to Knowledge articles, and define Data Category filters in the Agentforce Data Library.Salesforce Knowledge uses Data Categories to organize articles (e.g., by topic or type). In Agentforce, when configuring a Data Library with Knowledge, you can apply Data Category filters to limit which articles the agent accesses. For the "Answer Questions with Knowledge" action, this ensures the agent only retrieves articles within the specified categories, aligning with UC's goal. This is a native, documented solution, making it the correct answer.
Why Option C is Correct:
Using Data Categories and filters in the Data Library is the recommended, scalable way to limit Knowledge article access for agent actions, as per Salesforce documentation.
References:
Salesforce Agentforce Documentation: Data Library > Knowledge Filters- Describes Data Category filtering.
Trailhead: Ground Your Agentforce Prompts- Covers limiting Knowledge scope.
Salesforce Help: Knowledge in Agentforce- Recommends categories for access control.
NEW QUESTION # 324
Universal Containers (UC) noticed an increase in customer contract cancellations in the last few months. UC is seeking ways to address this issue by implementing a proactive outreach program to customers before they cancel their contracts and is asking the Salesforce team to provide suggestions.
Which use case functionality of Model Builder aligns with UC's request?
Answer: A
Explanation:
Customer churn prediction is the best use case for Model Builder in addressing Universal Containers' concerns about increasing customer contract cancellations. By implementing a model that predicts customer churn, UC can proactively identify customers who are at risk of canceling and take action to retain them before they decide to terminate their contracts. This functionality allows the business to forecast churn probability based on historical data and initiate timely outreach programs.
Option B is correct because customer churn prediction aligns with UC's need to reduce cancellations through proactive measures.
Option A (product recommendation prediction) is unrelated to contract cancellations.
Option C (contract renewal date prediction) addresses timing but does not focus on predicting potential cancellations.
Salesforce Model Builder Use Case Overview: https://help.salesforce.com/s/articleView?id=sf.
model_builder_use_cases.htm
NEW QUESTION # 325
Choose 1 option.
Universal Containers needs to create Data Cloud reports to understand agent behavior.
Which data lake object (DLO) represents an overarching container capturing contiguous interactions with one or more Al agents?
Answer: C
Explanation:
Per the AgentForce Data Cloud Integration Guide, the AI Agent Session (AIAGENTSESSION) object represents an overarching container that tracks a continuous interaction between one or more AI agents and a user. The document describes: "AI Agent Session is the parent container for a contiguous set of AI Agent interactions. It captures metadata, start and end times, and the relationship to individual messages exchanged during the session." Option A, AIAGENTINTERACTION, represents a single step or event within the session, while AIAGENTINTERACTIONMESSAGE (Option B) represents individual messages or exchanges within that interaction. Therefore, AIAGENTSESSION is the correct DLO for reporting on broader agent behavior and performance across an entire engagement.
References (AgentForce Documents / Study Guide):
* AgentForce Data Cloud Reporting Guide: "Understanding Agent DLO Hierarchy"
* AgentForce Study Notes: "AI Agent Session, Interaction, and Message Object Relationships"
* Salesforce Data Cloud for AI Agents: "AI Agent Session Overview"
NEW QUESTION # 326
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:
Comprehensive and Detailed In-Depth 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.
* Option A: Switching to a "Flex" template type might sound plausible, but Salesforce documentation does not define "Flex" as a specific template type for handling token variability in this context (there are Flow-based templates, but they're unrelated to token limits). This option is a distractor and not a verified solution.
* Option C: The LLM's token processing capacity is fixed per model (e.g., a set limit like 128,000 tokens for advanced models) and does not vary with user demand. Demand might affect performance or availability, but not the token limit itself.
Option B is the correct answer because it accurately identifies the dynamic nature of the prompt template as the root cause of variable token counts leading to random failures.
References:
* Salesforce Agentforce Documentation: "Prompt Templates" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_prompt_templates.htm&type=5)
* Trailhead: "Build Prompt Templates for Agentforce" (https://trailhead.salesforce.com/content/learn
/modules/build-prompt-templates-for-agentforce)
NEW QUESTION # 327
Universal Containers' current AI data masking rules do not align with organizational privacy and security policies and requirements.
What should An Agentforce recommend to resolve the issue?
Answer: B
Explanation:
WhenUniversal Containers' AI data masking rulesdo not meet organizational privacy and security standards, the Agentforce Specialist should configure thedata maskingrules within theEinstein Trust Layer.
TheEinstein Trust Layerprovides a secure and compliant environment where sensitive data can be masked or anonymized to adhere to privacy policies and regulations.
* Option A, enabling data masking for sandbox refreshes, is related to sandbox environments, which are separate from how AI interacts with production data.
* Option C, adding masking rules in the LLM setup, is not appropriate because data masking is managed through theEinstein Trust Layer, not the LLM configuration.
The Einstein Trust Layer allows for more granular control over what data is exposed to the AI model and ensures compliance with privacy regulations.
Salesforce Agentforce Specialist References:
For more information, refer to:https://help.salesforce.com/s/articleView?id=sf.
einstein_trust_layer_data_masking.htm
NEW QUESTION # 328
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