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NO.1 What is an example of ethical debt?

A. Violating a data privacy law and falling to pay fines
B. Launching an AI feature after discovering a harmful bias
C. Delaying an AI product launch to retrain an AI data model
Answer: B
Explanation:
"Launching an AI feature after discovering a harmful bias is an example of ethical debt. Ethical debt is
a term that describes the potential harm or risk caused by unethical or irresponsible decisions or
actions related to AI systems. Ethical debt can accumulate over time and have negative consequences
for users, customers, partners, or society. For example, launching an AI feature after discovering a
harmful bias can create ethical debt by exposing users to unfair or inaccurate results that may affect
their trust, satisfaction, or well-being."

NO.2 Cloud Kicks' latest email campaign is struggling to attract new customers.
How can AI increase the company's customer email engagement?
A. Create personalized emails
B. Resend emails to inactive recipients
C. Remove invalid email addresses
Answer: A
Explanation:
AI can significantly increase customer email engagement by creating personalized emails. Salesforce
Einstein AI enhances email marketing campaigns by analyzing customer data and past interactions to
tailor the content, timing, and recommendations within emails. This personalization leads to higher
engagement rates as emails resonate more closely with individual preferences and behaviors.
Salesforce Marketing Cloud provides tools to leverage AI for crafting personalized email campaigns,
ensuring that emails are relevant and appealing to recipients. For more insights into how AI can be
used to enhance email marketing, see the Salesforce Marketing Cloud page at Salesforce Marketing
Cloud Email Studio.

NO.3 Why is it critical to consider privacy concerns when dealing with AI and CRM data?
A. Ensures compliance with laws and regulations
B. Confirms the data is accessible to all users
C. Increases the volume of data collected
Answer: A
Explanation:
"It is critical to consider privacy concerns when dealing with AI and CRM data because it ensures
compliance with laws and regulations. Data privacy is the right of individuals to control how their
personal data is collected, used, shared, or stored by others. Data privacy laws and regulations are
legal frameworks that define and enforce the rights and obligations of data subjects, data controllers,
and data processors regarding personal data. Data privacy laws and regulations vary by country,
region, or industry, and may impose different requirements or restrictions on how AI and CRM data
can be handled."

NO.4 Cloud Kicks plans to use automated chat as its primary support channel.
Which Einstein feature should they use?

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A. Discovery
B. Bots
C. Next Best Action
Answer: B
Explanation:
For Cloud Kicks, using automated chat as the primary support channel, the recommended Einstein
feature is Bots. Einstein Bots are designed to automate customer interactions on common issues
through chat and messaging platforms. They can handle routine requests, provide quick answers to
frequently asked questions, and escalate more complex issues to human agents. Using Einstein Bots
helps improve customer service efficiency and speed, leading to enhanced customer satisfaction. To
learn more about setting up and optimizing Einstein Bots for a business, you can visit the Salesforce
documentation on Einstein Bots at Salesforce Einstein Bots.

NO.5 Which best describes the difference between predictive AI and generative Al?
A. Predictive AT uses machine learning to classify or predict outputs from its input data whereas
generative Al does not use machine learning to generate its output.
B. Predictive Al uses machine learning to classify or predict outputs from its input data whereas
generative Al uses machine learning to generate new and original output for 4 given input
C. Predictive Al and generative Al have the same capabilities but differ in the type of input they
receive; predictive AT receives raw data whereas generative AT receives natural language.
Answer: B
Explanation:
Predictive AI and generative AI represent two different applications of machine learning
technologies.
Predictive AI focuses on making predictions based on historical data. It analyzes past data to forecast
future outcomes, such as customer churn or sales trends. On the other hand, generative AI is
designed to generate new and original outputs based on the learned data patterns. This includes
tasks like creating new images, text, or music that resemble the training data but do not duplicate it.
Both types of AI use machine learning, but their objectives and outputs are distinct. For detailed
differences and applications in a Salesforce context, Salesforce's guide on AI technologies is a helpful
resource, accessible at Salesforce AI Technologies.

NO.6 Which data does Salesforce automatically exclude from marketing Cloud Einstein engagement
model training to mitigate bias and ethic...
A. Geographic
B. Geographic
C. Cryptographic
Answer: B
Explanation:
"Demographic data is the data that Salesforce automatically excludes from Marketing Cloud Einstein
engagement model training to mitigate bias and ethical concerns. 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

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ethics of AI systems. Salesforce excludes demographic data from Marketing Cloud Einstein
engagement model training to mitigate bias and ethical concerns by ensuring that the models are
based on behavioral data rather than personal data."

NO.7 What is Salesforce's Trusted AI Principle of Transparency?

A. The customization of AT features to meet specific business requirements
B. The integration of AT models with Salesforce workflows
C. The clear and understandable explanation of Al decisions and actions
Answer: C
Explanation:
Salesforce's Trusted AI Principle of Transparency emphasizes the importance of providing clear and
understandable explanations of AI decisions and actions. This principle ensures that users can
understand how AI conclusions are drawn, which is crucial for trust and accountability, especially in
business applications where AI decisions can have significant impacts. Transparency helps mitigate
the "black box" nature of AI systems by making them more interpretable and allows for better
oversight, compliance, and alignment with ethical guidelines. Salesforce elaborates on these
principles in their ethical AI practices, which can be further explored at Salesforce Ethical AI.

NO.8 What is the rile of data quality in achieving AI business Objectives?

A. Data quality is unnecessary because AI can work with all data types.
B. Data quality is required to create accurate AI data insights.
C. Data quality is important for maintain Ai data storage limits
Answer: B
Explanation:
"Data quality is required to create accurate AI data insights. 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 quality can also affect the accuracy and validity of AI data insights,
as they reflect the quality of the data used or generated by AI systems."

NO.9 How does data quality impact the trustworthiness of Al-driven decisions?
A. The use of both low-quality and high-quality data can improve the accuracy and reliability of AI-
driven decisions.
B. High-quality data improves the reliability and credibility of Al-driven decisions, fostering trust
among users.
C. Low-quality data reduces the risk of overfitting the model, improving the trustworthiness of the
predictions.
Answer: B
Explanation:
"High-quality data improves the reliability and credibility of AI-driven decisions, fostering trust among
users.
High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the
AI task.
High-quality data can improve the performance and reliability of AI systems, as they have enough and
correct information to learn from and make accurate predictions. High-quality data can also improve

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the trustworthiness of AI-driven decisions, as users can have more confidence and satisfaction in
using AI systems."

NO.10 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
Answer: C
Explanation:
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.

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