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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Exam Format: | Multiple Choice, Multiple Select, Scenario-Based |
| Available Languages: | English |
| Exam Duration: | 105 minutes |
| Certificate Validity Period: | 1 year (requires annual maintenance) |
| Passing Score: | 73% |
| Related Certifications: | Salesforce Certified AI Specialist |
| Real Exam Qty: | 60-65 |
| Exam Price: | $200 USD |
| Recommended Training: | Trailhead Agentforce Specialist Certification Prep |
| Exam Registration: | Salesforce Certification Registration Pearson VUE Scheduling |
| Sample Questions: | Salesforce Agentforce-Specialist Sample Questions |
| Exam Way: | Online proctored or onsite at Pearson VUE test centers |
| Pre Condition: | No formal prerequisites; recommended: 6 months experience with Agentforce or Salesforce AI features |
| Official Syllabus URL: | https://trailhead.salesforce.com/credentials/certification-detail-print/?certificationId=286 |
>> Detailed Agentforce-Specialist Study Dumps <<
The Salesforce Agentforce-Specialist certification exam is one of the hottest and career-oriented Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) exams. With the Salesforce Certified Agentforce Specialist (AI-201) (Agentforce-Specialist) exam you can validate your skills and upgrade your knowledge level. By doing this you can learn new in-demand skills and gain multiple career opportunities. To do this you just need to enroll in the Salesforce Agentforce-Specialist Certification Exam and put all your efforts to pass this important Salesforce Agentforce-Specialist Exam Questions.
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NEW QUESTION # 318
Universal Containers (UC) is using standard Service AI Grounding. UC created a custom rich text field to be used with Service AI Grounding.
What should UC consider when using standard Service AI Grounding?
Answer: A
Explanation:
Service AI Grounding retrieves data from Salesforce objects to ground AI-generated responses.Key considerations:
* Field Types: Standard Service AI Grounding supports String and Text Area fields. Custom rich text fields (e.g., RichTextArea) are not supported, making Option B correct.
* Objects: While Service AI Grounding primarily uses Case and Knowledge objects (Option A), the limitation here is the field type, not the object.
* Visibility: Service AI Grounding respects user permissions and sharing settings unless overridden (Option C is incorrect).
References:
* Salesforce Help: Service AI Grounding Requirements
* Explicitly states support for "Text Area and String fields" only.
NEW QUESTION # 319
What is a SalesforceAgentforce Specialistable to configure in Data Masking within the Einstein Trust Layer?
Answer: A
Explanation:
In the Einstein Trust Layer, the SalesforceAgentforce Specialistcan configure privacy data entities to be masked (Option C). This ensures sensitive or personally identifiable information (PII) is obfuscated when processed by AI models.
* Data Masking Configuration:
* TheAgentforce Specialistdefines which fields or data types (e.g., email, phone number, Social Security Number) should be masked. For example, masking the Email field in a prompt response to protect user privacy.
* This is done through declarative settings in Salesforce, where entities (standard or custom fields) are flagged for masking.
* Why Other Options Are Incorrect:
* A. Profiles exempt from masking: Exemptions are typically managed via permissions (e.g., field-level security), not directly within Einstein Trust Layer's Data Masking settings.
* B. Encryption keys for masking: Encryption is separate from masking. Masking involves obfuscation (e.g., replacing "john@example.com" with "@"), not encryption, which uses keys to secure data.
References:
* Einstein Trust Layer Documentation: States that Data Masking allows admins to "define which fields should be masked to protect sensitive data."
* Trailhead Module: "Einstein Trust Layer Basics" explains configuring privacy entities for masking.
* Salesforce Help Article: "Secure AI with Einstein Trust Layer" details masking configurations for privacy compliance.
NEW QUESTION # 320
Support agents at Universal Containers are using Agentforce to find troubleshooting information. They've reported that the agent frequently provides knowledge articles that are outdated, even when newer versions of the articles are available. The administrator has confirmed that all articles are correctly chunked and indexed.
Which configuration change in the Data Cloud hybrid search index best addresses this problem?
Answer: C
Explanation:
The AgentForce Data Cloud Retrieval and Ranking Guide highlights that when outdated Knowledge articles appear before newer ones, administrators should configure ranking factors that prioritize content based on recency. The documentation specifies: "Adding a recency ranking factor using the LastModifiedDate or LastPublishedDate fields ensures the retrieval prioritizes the most up-to-date documents, improving response relevance." Option A (disabling keyword index) would remove precision in retrieval and does not address recency.
Option B (changing chunking strategy) affects data segmentation, not ranking order.
Therefore, Option C - adding a ranking factor for recency - is the correct way to ensure updated articles are prioritized.
References (AgentForce Documents / Study Guide):
* AgentForce Data Cloud Hybrid Search Configuration Guide: "Applying Recency Ranking"
* AgentForce Knowledge Management Handbook: "Prioritizing Updated Articles in Search"
* AgentForce Study Guide: "Ranking and Weighting Strategies for Knowledge Retrieval"
NEW QUESTION # 321
When configuring a prompt template, an Agentforce Specialist previews the results of the prompt template they've written. They see two distinct text outputs: Resolution and Response. Which information does the Resolution text provide?
Answer: A
Explanation:
In Salesforce Agentforce, when previewing a prompt template, the interface displays two outputs: Resolution and Response. These terms relate to how the prompt is processed and evaluated, particularly in the context of the Einstein Trust Layer, which ensures AI safety, compliance, and auditability. The Resolution text specifically refers to the full text that is sent to the Trust Layer for processing, monitoring, and governance (Option A). This includes the constructed prompt (with grounding data, instructions, and variables) as it's submitted to the large language model (LLM), along with any Trust Layer interventions (e.g., masking, filtering) applied before or after LLM processing. It's a comprehensive view of the input/output flow that the Trust Layer captures for auditing and compliance purposes.
* Option B: The "Response" output in the preview shows the LLM's generated text based on the sample record, not the Resolution. Resolution encompasses more than just the LLM response-it includes the entire payload sent to the Trust Layer.
* Option C: While the Trust Layer does mask sensitive data (e.g., PII) as part of its guardrails, the Resolution text doesn't specifically isolate "which sensitive data is masked." Instead, it shows the full text, including any masked portions, as processed by the Trust Layer-not a separate masking log.
* Option A: This is correct, as Resolution provides a holistic view of the text sent to the Trust Layer, aligning with its role in monitoring and auditing the AI interaction.
Thus, Option A accurately describes the purpose of the Resolution text in the prompt template preview.
:
Salesforce Agentforce Documentation: "Preview Prompt Templates" (Salesforce Help: https://help.salesforce.
com/s/articleView?id=sf.agentforce_prompt_preview.htm&type=5)
Salesforce Einstein Trust Layer Documentation: "Trust Layer Outputs" (https://help.salesforce.com/s
/articleView?id=sf.einstein_trust_layer.htm&type=5)
NEW QUESTION # 322
Universal Containers wants to use an Al agent to answer questions about warranties, Warranty information has already been uploaded as unstructured data in Data Cloud. When answering user questions, the results must be filterable by product line and ranked by recent updates.
Which approach should the Agentforce Specialist implement?
Answer: C
Explanation:
According to the AgentForce and Einstein Studio Integration Guide, when a business requires custom ranking or filtering logic (such as by product line and recency), the correct solution is to build a custom retriever in Einstein Studio. The documentation describes: "Custom retrievers in Einstein Studio enable configuration of metadata filters (e.g., product line) and custom ranking functions such as recency or relevance scoring. This allows fine-tuned control over retrieval beyond the default retriever's capabilities." Option A, the default retriever, provides general ranking and does not natively apply custom filters. Option C, applying semantic embeddings with default filters, is useful for general search optimization but lacks custom ranking logic.
Therefore, Option B aligns with Salesforce's prescribed method for fine-tuned retrieval control in enterprise use cases requiring metadata-based and recency ranking.
References (AgentForce Documents / Study Guide):
AgentForce Einstein Studio Guide: "Building Custom Retrievers with Metadata and Ranking" AgentForce Data Cloud Configuration Notes: "Filtering and Ranking in Custom Retrieval" AgentForce Study Guide: "Advanced Retrieval Customization in Einstein Studio"
NEW QUESTION # 323
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