Which action should be taken to develop and implement trusted generated AI with Salesforce’s safety guideline in mind?

1. A. Develop right-sized models to reduce our carbon footprint.
2. B. Create guardrails that mitigates toxicity and protect PII
3. C. Be transparent when AI has created and automatically delivered content.

Correct Answer: B
“Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generative AI with Salesforce’s safety guideline in mind. Salesforce’s safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creating guardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AI systems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems.”

A business analyst (BA) is preparing a new use case for Al. They run a report to check for null values in the attributes they plan to use.
Which data quality component Is the BA verifying by checking for null values?

1. A. Duplication
2. B. Usage
3. C. Completeness

Correct Answer: C
By checking for null values, a business analyst (BA) is verifying the data quality component of completeness. Completeness refers to the absence of missing values or gaps in the data, which is essential for the accuracy and reliability of reports and analytics used in AI models. Null values can indicate incomplete data, which may adversely affect the performance of AI applications by leading to incorrect predictions or insights. Salesforce emphasizes the importance of data completeness for effective data analysis and provides tools for data quality assessment and improvement. Details on handling data completeness in Salesforce can be explored at Salesforce Help Data Management.

What can bias in AI algorithms in CRM lead to?

1. A. Personalization and target marketing changes
2. B. Advertising cost increases
3. C. Ethical challenges in CRM systems

Correct Answer: C
“Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers’ identity or characteristics.”

A sales manager wants to improve their processes using AI in Salesforce? Which application of AI would be most beneficial?

1. A. Lead soring and opportunity forecasting
2. B. Sales dashboards and reporting
3. C. Data modeling and management

Correct Answer: A
“Lead scoring and opportunity forecasting are applications of AI that would be most beneficial for a sales manager who wants to improve their processes using AI in Salesforce. Lead scoring can help prioritize leads based on their likelihood to convert, while opportunity forecasting can help predict future sales or revenue based on historical data and trends. These applications of AI can help optimize sales processes by providing insights and recommendations that can increase sales efficiency and effectiveness.”

What are some of the ethical challenges associated with AI development?

1. A. Potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes
2. B. Implicit transparency of AI systems, which makes It easy for users to understand andtrust their decisions
3. C. Inherent neutrality of AI systems, which eliminates any potential for human bias in decision-making

Correct Answer: A
“Some of the ethical challenges associated with AI development are the potential for human bias in machine learning algorithms and the lack of transparency in AI decision-making processes. Human bias can arise from the data used to train the models, the design choices made by the developers, or the interpretation of the results by the users. Lack of transparency can make it difficult to understand how and why AI systems make certain decisions, which can affect trust, accountability, and fairness.”