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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

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

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Salesforce Certified AI Associate Exam Sample Questions (Q57-Q62):

NEW QUESTION # 57
What is a Key consideration regarding data quality in AI implementation?

  • A. Data's role in training and fine-tuning Salesforce AI models
  • B. Techniques from customizing AI features in Salesforce
  • C. Integration process of AI models with Salesforce workflows

Answer: A

Explanation:
"Data's role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. 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. Data's role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery."


NEW QUESTION # 58
What is the key difference between generative and predictive AI?

  • A. Generative AI finds content similar to existing data and predictive AI analyzes existing data.
  • B. Generative AI analyzes existing data and predictive AI creates new content based on existing data.
  • C. Generative AI creates new content based on existing data and predictive AI analyzes existing data.

Answer: C

Explanation:
Explanation
"The key difference between generative and predictive AI is that generative AI creates new content based on existing data and predictive AI analyzes existing data. Generative AI is a type of AI that can generate novel content such as images, text, music, or video based on existing data or inputs. Predictive AI is a type of AI that can analyze existing data or inputs and make predictions or recommendations based on patterns or trends."


NEW QUESTION # 59
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce's Trusted AI Principle of Transparency?

  • A. Flag sensitive variables and their proxies to prevent discriminatory lending practices.
  • B. Incorporate customer feedback into the model's continuous training.
  • C. Communicate how risk factors such as credit score can impact customer eligibility.

Answer: A

Explanation:
Explanation
"Flagging sensitive variables and their proxies to prevent discriminatory lending practices is how they should implement Salesforce's Trusted AI Principle of Transparency. Transparency is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for clarity and openness in how they work and why they make certain decisions. Transparency also means that AI users should be able to access relevant information and documentation about the AI systems they interact with. Flagging sensitive variables and their proxies means identifying and marking variables that can potentially cause discrimination or unfair treatment based on a person's identity or characteristics, such as age, gender, race, income, or credit score. Flagging sensitive variables and their proxies can help implement Transparency by allowing users to understand and evaluate the data used or generated by AI systems."


NEW QUESTION # 60
How does a data quality assessment impact business outcome for companies using AI?

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

Answer: A

Explanation:
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."


NEW QUESTION # 61
What is the role of Salesforce Trust AI principles in the context of CRM system?

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

Answer: C

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 practicesfor 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 # 62
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