Salesforce Salesforce-AI-Associate Testfagen, Salesforce-AI-Associate Exam

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Salesforce Salesforce-AI-Associate Prüfungsplan:

Thema	Einzelheiten
Thema 1	AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.
Thema 2	Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.
Thema 3	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 the Trusted AI Principles of Salesforce to given scenarios.
Thema 4	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.

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Salesforce-AI-Associate Exam & Salesforce-AI-Associate Übungsmaterialien

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Salesforce Certified AI Associate Exam Salesforce-AI-Associate Prüfungsfragen mit Lösungen (Q25-Q30):

25. Frage
What is the significance of explainability of trusted AI systems?

A. Increases the complexity of AI models
B. Enhances the security and accuracy of AI models
C. Describes how Al models make decisions

Antwort: C

Begründung:
The significance of the explainability of trusted AI systems is that it describes how AI models make decisions. Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI. This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable. More details about Salesforce's approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.

26. Frage
Which type of AI can enhance customer service agents' email responses by analyzing the written content of previous emails?

A. Machine learning
B. Natural language processing
C. Deep learning

Antwort: B

Begründung:
Natural language processing (NLP) is the type of AI that can enhance customer service agents' email responses by analyzing the written content of previous emails. NLP technologies interpret and generate human language, allowing AI systems to understand, respond to, and even anticipate customer needs based on email interactions. This capability helps in crafting more relevant, accurate, and personalized email responses, improving customer service quality. Salesforce utilizes NLP in its Einstein AI platform to augment various customer service functions. More about Salesforce Einstein's NLP capabilities can be found on the Salesforce Einstein page at Salesforce Einstein NLP.

27. Frage
Cloud Kicks wants to use Einstein Prediction Builder to determine a customer's likelihood of buying specific products; however, data quality is a...
How can data quality be assessed quality?

A. Build reports to expire the data quality.
B. Leverage data quality apps from AppExchange
C. Build a Data Management Strategy.

Antwort: B

Begründung:
Explanation
"Leveraging data quality apps from AppExchange is how data quality can be assessed. Data quality is the degree to which data is accurate, complete, consistent, relevant, and timely for the AI task. Data quality can affect the performance and reliability of AI systems, as they depend on the quality of the data they use to learn from and make predictions. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce."

28. Frage
What should be done to prevent bias from entering an AI system when training it?

A. Import diverse training data.
B. Include Proxy variables.
C. Use alternative assumptions.

Antwort: A

Begründung:
"Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features and patterns that are relevant for the AI task. Diversetraining data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data."

29. Frage
How does a data quality assessment impact business outcome for companies using AI?

A. Improves the speed of AI recommendations
B. Provides a benchmark for AI predictions
C. Accelerates the delivery of new AI solutions

Antwort: B

Begründung:
Explanation
"A data quality assessment impacts business outcomes for companies using AI by providing a benchmark for AI predictions. A data quality assessment is a process that measures and evaluates the quality of data for a specific purpose or task. A data quality assessment can help identify and address any issues or gaps in the data quality dimensions, such as accuracy, completeness, consistency, relevance, and timeliness. A data quality assessment can impact business outcomes for companies using AI by providing a benchmark for AI predictions, as it can help ensure that the predictions are based on high-quality data that reflects the true state or condition of the target population or domain."

30. Frage
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