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| Certification Vendor: | Salesforce |
|---|---|
| Exam Name: | Salesforce Certified Agentforce Specialist (AI-201) |
| Exam Number: | AI-201 |
| Exam Price: | USD 200 |
| Exam Duration: | 105 minutes |
| Real Exam Qty: | 60 multiple-choice questions + up to 5 unscored questions |
| Passing Score: | 72%-73% |
| Exam Format: | Multiple Choice, Multiple Select, Scenario-based Questions |
| Related Certifications: | Salesforce AI Associate (retired) Salesforce Platform Administrator Salesforce Platform App Builder Salesforce AI Specialist (retired) |
| 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 |
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NEW QUESTION # 121
An Agentforce at Universal Containers is trying to set up a new Field Generation prompt template. They take the following steps.
1. Create a new Field Generation prompt template.
2. Choose Case as the object type.
3. Select the custom field AI_Analysis_c as the target field.
After creating the prompt template, the Agentforce Specialist saves, tests, and activates it. Howsoever, when they go to a case record, the AI Analysis field does not show the (Sparkle) icon on the Edit pencil. When the Agentforce Specialist was editing the field, it was behaving as a normal field.
Which critical step did the Agentforce Specialist miss?
Answer: A
Explanation:
For Field Generation prompt templates to display the Sparkle icon (indicating AI-generated content), the target field must be explicitly associated with the prompt template on the Lightning page layout. Even if the prompt template is activated, failing to add the field to the page layout and link it to the template will result in the field behaving as a standard field. Salesforce documentation emphasizes that page layout configuration is mandatory to enable AI-driven field interactions.
* Reactivating the layout (A) is unnecessary unless the layout itself was modified after activation.
* Case objects are supported for Field Generation (B is incorrect).
Reference:
Salesforce Help Article: Configure Field Generation Prompt Templates ("Associating Fields with Page Layouts" section).
Einstein GPT Implementation Guide: "Enabling AI-Generated Fields in Lightning Pages."
NEW QUESTION # 122
Universal Containers wants to reduce overall customer support handling time by minimizing the time spent typing routine answers for common questions in-chat, and reducing the post-chat analysis by suggesting values for case fields. Which combination of Agentforce for Service features enables this effort?
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) aims to streamline customer support by addressing two goals: reducing in-chat typing time for routine answers and minimizing post-chat analysis by auto-suggesting case field values. In Salesforce Agentforce for Service,Einstein Reply RecommendationsandCase Classification(Option A) are the ideal combination to achieve this.
* Einstein Reply Recommendations: This feature uses AI to suggest pre-formulated responses based on chat context, historical data, and Knowledge articles. By providing agents with ready-to-use replies for common questions, it significantly reduces the time spent typing routine answers, directly addressing UC's first goal.
* Case Classification: This capability leverages AI to analyze case details (e.g., chat transcripts) and suggest values for case fields (e.g., Subject, Priority, Resolution) during or after the interaction. By automating field population, it reduces post-chat analysis time, fulfilling UC's second goal.
* Option B: While "Einstein Reply Recommendations" is correct for the first part, "Case Summaries" generates a summary of the case rather than suggesting specific field values. Summaries are useful for documentation but don't directly reduce post-chat field entry time.
* Option C: "Einstein Service Replies" is not a distinct, documented feature in Agentforce (possibly a distractor for Reply Recommendations), and "Work Summaries" applies more to summarizing work orders or broader tasks, not case field suggestions in a chat context.
* Option A: This combination precisely targets both in-chat efficiency (Reply Recommendations) and post-chat automation (Case Classification).
Thus, Option A is the correct answer for UC's needs.
References:
* Salesforce Agentforce Documentation: "Einstein Reply Recommendations" (Salesforce Help:
https://help.salesforce.com/s/articleView?id=sf.einstein_reply_recommendations.htm&type=5)
* Salesforce Agentforce Documentation: "Case Classification" (Salesforce Help:https://help.salesforce.
com/s/articleView?id=sf.case_classification.htm&type=5)
* Trailhead: "Agentforce for Service" (https://trailhead.salesforce.com/content/learn/modules/agentforce- for-service)
NEW QUESTION # 123
An Agentforce at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team's time. UC wants to help prep the sales team for the calls by summarizing past purchases, interests in products shown by the Contact captured via Data Cloud, and a recap of past email and phone conversations for which there are transcripts.
Which approach should the Agentforce Specialist recommend to achieve this use case?
Answer: A
Explanation:
For no-code implementations, Prompt Builder allows Agentforce Specialists to create prompt templates that dynamically ground responses in Salesforce CRM data (e.g., past purchases) and Data Cloud insights (e.g., product interests) without custom coding. The standard foundation model (e.g., Einstein GPT) can synthesize this data into summaries, leveraging structured and unstructured sources (e.g., email/phone transcripts). Fine- tuning (B) or custom models (C) require code and are unnecessary here, as the use case does not involve unique data patterns requiring model retraining.
Reference:
Salesforce Help Article: Prompt Builder for No-Code AI ("Grounding in CRM and Data Cloud" section).
Einstein GPT Implementation Guide: "Generating Summaries with Pre-Built Models."
NEW QUESTION # 124
Universal Containers' Agent Action includes several Apex classes for the new Agentforce Agent. What is an important consideration when deploying Apex that is invoked by an Agent Action?
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:Universal Containers (UC) is using Apex classes within an Agent Action for their Agentforce Agent. Deploying Apex in Salesforce has specific requirements, especially when tied to Agentforce functionality. Let's evaluate the options.
* Option A: The Apex classes must have at least 75% code coverage from unit tests, and all dependencies must be in the deployment package.Salesforce enforces a strict requirement that all Apex classes must achieve at least 75% code coverage from unit tests for deployment to production, regardless of their use case (e.g., Agentforce, triggers, or web services). Additionally, when Apex is invoked by an Agent Action (e.g., via a Flow or direct invocation), all dependencies (e.g., referenced classes, objects) must be included in the deployment package to ensure functionality. This is a standard deployment consideration in Salesforce and applies to Agentforce, making this the correct answer.
* Option B: Apex classes invoked by an Agent Action may be deployed with less than 75% test coverage as long as the agent is not activated in production.Salesforce's 75% code coverage requirement is mandatory for production deployment, regardless of whether the agent is activated.
There's no exemption based on activationstatus-coverage is enforced at the deployment stage. This option is incorrect and contradicts Salesforce's Apex deployment rules.
* Option C: The Apex classes may bypass the 75% code coverage requirement as long as they are only used by the agent.No such bypass exists in Salesforce. The 75% code coverage rule applies universally to all Apex in production, including classes used by Agentforce. Agent-specific usage doesn' t waive this requirement, making this incorrect.
Why Option A is Correct:The 75% code coverage requirement and inclusion of dependencies are fundamental Salesforce deployment rules, applicable to Apex in Agent Actions. This ensures reliability and functionality in production, as per official documentation.
References:
* Salesforce Agentforce Documentation: Agent Builder > Custom Actions > Apex- Notes standard Apex deployment rules apply.
* Salesforce Developer Guide: Apex Testing- Confirms 75% coverage requirement.
* Trailhead: Deploy Apex Code- Emphasizes coverage and dependencies for production.
NEW QUESTION # 125
What happens when a chunk of text is vectorized?
Answer: B
Explanation:
The correct answer is A because vectorization converts text chunks into numerical embeddings that capture semantic meaning. In Agentforce and Data 360 retrieval scenarios, large documents are first broken into smaller chunks or passages, and those chunks are then converted into vectors. The retrieval engine can compare a user's question or prompt with those vectors to find content that is meaningfully similar, not merely keyword-matched. Option B is wrong because vectorization is not encryption; security and storage controls are separate platform concerns. Option C is wrong because vectorization is not a compression mechanism and is not used primarily to reduce file size or storage cost. Salesforce Data 360 documentation describes chunking as creating manageable semantic units and vectorization as converting those chunks into numeric representations of text.
NEW QUESTION # 126
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