FREE PDF SALESFORCE - UPDATED SALESFORCE-AI-ASSOCIATE - SALESFORCE CERTIFIED AI ASSOCIATE EXAM TEST ONLINE

Free PDF Salesforce - Updated Salesforce-AI-Associate - Salesforce Certified AI Associate Exam Test Online

Free PDF Salesforce - Updated Salesforce-AI-Associate - Salesforce Certified AI Associate Exam Test Online

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Salesforce Certified AI Associate Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
What is the role of Salesforce Trust AI principles in the context of CRM system?

  • A. Guiding ethical and responsible use of AI
  • B. Providing a framework for AI data model accuracy
  • C. Outlining the technical specifications for AI integration

Answer: A

Explanation:
Explanation
"The role of Salesforce Trust AI principles in the context of CRM systems is guiding ethical and responsible use of AI. Salesforce Trust AI principles are a set of guidelines and best practices for developing and using AI systems in a responsible and ethical way. The principles include Accountability, Fairness & Equality, Transparency & Explainability, Privacy & Security, Reliability & Safety, Inclusivity & Diversity, Empowerment & Education. The principles aim to ensure that AI systems are aligned with the values and interests of customers, partners, and society."


NEW QUESTION # 36
What is an example of ethical debt?

  • A. Delaying an AI product launch to retrain an AI data model
  • B. Violating a data privacy law and failing to pay fines
  • C. Launching an AI feature after discovering a harmful bias

Answer: C

Explanation:
Ethical debt refers to the long-term negative consequences of prioritizing speed or convenience over responsible AI development practices. Ethical debt accumulates when AI systems are deployed despite known ethical concerns, such as bias, privacy violations, or transparency issues.
Option A (Correct): Launching an AI feature after discovering harmful bias is a clear example of ethical debt because it disregards the ethical obligation to ensure fairness and non-discrimination in AI outcomes. Ignoring bias can lead to systemic issues that are difficult and costly to correct later.
Option B (Incorrect): Violating a data privacy law and failing to pay fines is a legal issue rather than an example of ethical debt. While related, ethical debt pertains more to AI decision-making and development choices.
Option C (Incorrect): Delaying an AI product launch to retrain an AI model is a responsible action that helps avoid ethical debt, rather than an example of it. This demonstrates an effort to mitigate bias and improve AI fairness before deployment.


NEW QUESTION # 37
To avoid introducing unintended bias to an AI model, which type of data should be omitted?

  • A. Transactional
  • B. Engagement
  • C. Demographic

Answer: C

Explanation:
Explanation
"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 fairness and ethics of AI systems."


NEW QUESTION # 38
Which Einstein capability uses emails to create content for Knowledge articles?

  • A. Predict
  • B. Generate
  • C. Discover

Answer: B

Explanation:
Explanation
"Einstein Generate uses emails to create content for Knowledge articles. Einstein Generate is a natural language generation (NLG) feature that can automatically write summaries, descriptions, or recommendations based on data or text inputs. For example, Einstein Generate can analyze email conversations between agents and customers and generate draft articles for the Knowledge base."


NEW QUESTION # 39
What is a key challenge of human AI collaboration in decision-making?

  • A. Leads to move informed and balanced decision-making
  • B. Creates a reliance on AI, potentially leading to less critical thinking and oversight
  • C. Reduce the need for human involvement in decision-making processes

Answer: B

Explanation:
Explanation
"A key challenge of human-AI collaboration in decision-making is that it creates a reliance on AI, potentially leading to less critical thinking and oversight. Human-AI collaboration is a process that involves humans and AI systems working together to achieve a common goal or task. Human-AI collaboration can have many benefits, such as leveraging the strengths and complementing the weaknesses of both humans and AI systems.
However, human-AI collaboration can also pose some challenges, such as creating a reliance on AI, potentially leading to less critical thinking and oversight. For example, human-AI collaboration can create a reliance on AI if humans blindly trust or follow the AI recommendations without questioning or verifying their validity or rationale."


NEW QUESTION # 40
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