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Salesforce Salesforce-AI-Associate Prüfungsplan:

Thema	Einzelheiten
Thema 1	AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.
Thema 2	Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply Trusted AI Principles of Salesforce to given scenarios.
Thema 3	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Thema 4	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.

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Salesforce-AI-Associate Trainingsmaterialien: Salesforce Certified AI Associate Exam & Salesforce-AI-Associate Lernmittel & Salesforce Salesforce-AI-Associate Quiz

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Salesforce Certified AI Associate Exam Salesforce-AI-Associate Prüfungsfragen mit Lösungen (Q34-Q39):

34. Frage
What is an example of Salesforce's Trusted AI Principle of Inclusivity in practice?

A. Striving for model explain ability
B. Working with human rights experts
C. Testing models with diverse datasets

Antwort: C

Begründung:
"An example of Salesforce's Trusted AI Principle of Inclusivity in practice is testing models with diverse datasets.Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Testing models with diverse datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain."

35. Frage
Cloud Kicks wants to evaluate its data quality to ensure accurate and up-to-date records.
Which type of records negatively impact data quality?

A. Duplicate
B. Structured
C. Complete

Antwort: A

Begründung:
Duplicate records negatively impact data quality by creating inconsistencies and confusion in database management, leading to potential errors in customer relationship management (CRM) systems like Salesforce. Duplicates can skew analytics results, lead to inefficiencies in customer service, and result in redundant marketing efforts. Salesforce offers various tools to identify and merge duplicate records, thereby maintaining high data integrity. More about managing duplicate records in Salesforce and ensuring data quality can be found in Salesforce's documentation on duplicate management at Salesforce Duplicate Management.

36. Frage
A Salesforce administrator creates a new field to capture an order's destination country.
Which field type should they use to ensure data quality?

A. Picklist
B. Text
C. Number

Antwort: A

Begründung:
Explanation
"A picklist field type should be used to ensure data quality for capturing an order's destination country. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values."

37. Frage
How does AI which CRM help sales representatives better understand previous customer interactions?

A. Creates, localizes, and translates product descriptions
B. Triggers personalized service replies
C. Provides call summaries

Antwort: C

Begründung:
"Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls."

38. Frage
How does AI assist in lead qualification?

A. Automatically interacts with prospects
B. Creates personalized SMS campaigns
C. Scores leads based on customer data

Antwort: C

Begründung:
AI assists in lead qualification primarily by scoring leads based on customer data. This process, known as lead scoring, uses machine learning algorithms to evaluate leads against a set of predefined criteria that reflect potential interest and sales readiness. The scores assigned help sales teams prioritize their efforts toward leads most likely to convert, thus improving efficiency and success rates in sales activities. Salesforce AI enhances this process through features like Einstein Lead Scoring, which automatically calculates scores based on both historical conversion data and behavioral data from prospects. For further insights, Salesforce provides detailed documentation on lead scoring with AI at Salesforce Einstein Lead Scoring.

39. Frage
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