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Salesforce-AI-Associate German, Salesforce-AI-Associate Exam Fragen

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Salesforce Salesforce-AI-Associate Prüfungsplan:

Thema
Einzelheiten

Thema 1

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 2

AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.

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 Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.

>> Salesforce-AI-Associate German <<

Salesforce-AI-Associate Exam Fragen - Salesforce-AI-Associate Deutsch

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Salesforce Certified AI Associate Exam Salesforce-AI-Associate Prüfungsfragen mit Lösungen (Q79-Q84):

79. Frage
A consultant conducts a series of Consequence Scanning workshops to support testing diverse datasets.
Which Salesforce Trusted AI Principles is being practiced>

A. Accountability
B. Transparency
C. Inclusivity

Antwort: C

Begründung:
Explanation
"Conducting a series of Consequence Scanning workshops to support testing diverse datasets is an action that practices Salesforce's Trusted AI Principle of Inclusivity. Inclusivity is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences. Conducting Consequence Scanning workshops means engaging with various stakeholders to identify and assess the potential impacts and implications of AI systems on different groups or domains. Conducting Consequence Scanning workshops can help practice Inclusivity by ensuring that diverse datasets are used to test and evaluate AI systems."

80. Frage
What is a potential source of bias in training data for AI models?

A. The data is collected from a diverse range of sources and demographics.
B. The data is collected in area time from sources systems.
C. The data is skewed toward is particular demographic or source.

Antwort: C

Begründung:
Explanation
"A potential source of bias in training data for AI models is that the data is skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the model to a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform."

81. Frage
To avoid introducing unintended bias to an AI model, which type of data should be omitted?

A. Engagement
B. Transactional
C. Demographic

Antwort: C

Begründung:
"Demographic data should be omitted to avoid introducing unintended bias to an AI model. Demographic data is data that describes the characteristics of a population or a group of people, such as age, gender, race, ethnicity, income, education, or occupation. Demographic data can lead to bias if it is used to discriminate or treat people differently based on their identity or attributes. Demographic data can also reflect existing biases or stereotypes in society or culture, which can affect the fairnessand ethics of AI systems."

82. Frage
What is one way to achieve transparency in AI?

A. Communicate AI goals and objectives with those involved prior to all interactions.
B. Allow users to give feedback regarding the inferences the AI makes about them.
C. Establish an ethical and unbiased culture amongst those involved.

Antwort: B

Begründung:
Transparency in AI refers to making AI decisions understandable and accountable to users and stakeholders.
It involves explaining how AI models make decisions and ensuring that users can question or challenge AI outcomes.
Option A (Incorrect): While establishing an ethical and unbiased culture is essential for responsible AI development, it does not directly contribute to AI transparency. Transparency requires clear communication and user engagement.
Option B (Incorrect): Communicating AI goals and objectives is helpful but insufficient on its own.
Transparency also includes revealing AI decision-making processes and allowing user oversight.
Option C (Correct): Allowing users to give feedback regarding AI inferences ensures transparency by making AI decision-making accountable. Users can report errors, biases, or misunderstandings, helping improve AI fairness and reliability.

83. 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:
Explanation
"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 andpatterns that are relevant for the AI task. Diverse training 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."

84. Frage
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