Questions for the SALESFORCE AI ASSOCIATE were updated on : Dec 01 ,2025
Cloud Kicks uses Einstein to generate predictions but is not seeing accurate results. What is a
potential reason for this?
B
Explanation:
AI models rely on high-quality data to produce accurate and reliable predictions. Poor data quality—
such as missing values, inconsistent formatting, or biased data—can negatively impact AI
performance.
Option A (Incorrect): If Cloud Kicks is using Einstein AI, it is unlikely that they are using the wrong
product, as Einstein is designed for predictive analytics. The issue is more likely related to data
quality or model training.
Option B (Correct): Poor data quality is one of the most common reasons for inaccurate AI
predictions. If the input data contains errors, biases, or incomplete information, the AI model will
generate flawed insights. Regular data cleaning and preprocessing are essential for improving
prediction accuracy.
Option C (Incorrect): Having too much data does not necessarily result in inaccurate predictions. In
fact, more data can improve model performance if properly structured and cleaned. However, if the
data is noisy or unstructured, it may lead to inconsistencies.
Reference: Salesforce Einstein AI Implementation Guide
What is one way to achieve transparency in AI?
C
Explanation:
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.
Reference: Salesforce AI Trust & Transparency Principles
What is an example of ethical debt?
A
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.
Reference: Salesforce AI Ethics Guidelines & Responsible AI Development
In the context of Salesforce's Trusted Al Principles, what does the principle of Responsibility primarily
focus on?
A
Explanation:
The principle of Responsibility in Salesforce's Trusted AI Principles primarily focuses on ensuring that
AI is used ethically. This includes making sure that AI technologies are developed and implemented
in ways that are transparent, fair, and accountable, with a strong emphasis on the impact on
individuals and society. The principle encourages organizations to take responsibility for the
outcomes of their AI systems and to avoid unintended consequences that could harm users or
society.
Cloud Kicks wants to evaluate the quality of its sales data.
Which first step should they take for the data quality assessment?
C
Explanation:
The first step Cloud Kicks should take for data quality assessment is to identify business
objectives. This is crucial because understanding how the company uses customer data to support its
business objectives will guide the assessment process1. By identifying the business objectives, Cloud
Kicks can determine what customer data is required to support those objectives and how that data is
being used. This foundational step is essential before moving on to other aspects of data quality
assessment, such as running reports or planning territories. It aligns the data quality initiatives with
the company’s goals and ensures that the assessment is focused on areas that will drive business
value
What is a societal implication of excluding ethics in AI development?
C
Explanation:
Excluding ethics in AI development can lead to societal implications such as harm to marginalized
communities. When ethical considerations are not integrated into AI development, the resulting
technologies may perpetuate or amplify biases, leading to unfair treatment or discrimination against
certain groups. This can reinforce existing social inequalities and prevent these communities from
benefiting equally from the advancements in AI technology. Salesforce is committed to responsible
AI development and emphasizes the importance of ethical considerations in their development
practices to prevent such outcomes. Details on Salesforce’s approach to ethical AI and its importance
can be found at Salesforce Ethical AI.
What is Salesforce's Trusted AI Principle of Transparency?
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.
A sales manager wants to use AI to help sales representatives log their calls quicker and more
accurately.
Which functionality provides the best solution?
A
Explanation:
The best functionality to help sales representatives log their calls quicker and more accurately is the
use of AI-generated Call Summaries. This feature leverages AI to analyze voice data from sales calls
and automatically generate concise summaries and actionable insights, which are then logged into
the CRM system. This not only speeds up the process of recording call details but also enhances the
accuracy of the data captured, reducing the likelihood of human error and ensuring that important
details are not missed. Salesforce provides AI tools that integrate with telephony solutions to enable
these capabilities, enhancing the efficiency of sales operations. For more information on Salesforce
AI features like Einstein Call Coaching that support this functionality, visit Salesforce Einstein Call
Coaching.
Cloud Kicks prepares a dataset for an AI model and identifies some inconsistencies in the data.
What is the most appropriate action the company should take?
C
Explanation:
When inconsistencies in data are identified, the most appropriate action is to investigate these
inconsistencies and apply data quality techniques. Adjusting the AI model to accommodate poor
quality data or simply increasing the quantity of data without addressing the underlying issues does
not solve the problem and can lead to less reliable AI outputs. Proper data cleaning, normalization,
and validation are necessary steps to ensure that the data fed into an AI model is accurate and
reliable, thus enhancing the model’s performance. Salesforce provides guidelines on how to manage
and improve data quality, including practical steps for addressing data inconsistencies, detailed at
Improving Data Quality in Salesforce.
Cloud Kicks relies on data analysis to optimize its product recommendations for customers.
How will incomplete data quality impact the company's recommendations?
B
Explanation:
Incomplete data quality negatively impacts the accuracy of product recommendations made by
Cloud Kicks. If data is missing or incomplete, the AI models used for product recommendation may
not have enough information to accurately predict customer preferences and behavior. This leads to
recommendations that may not align well with customer needs, reducing customer satisfaction and
potentially affecting sales. Ensuring complete and accurate data is crucial for effective
recommendation systems. Salesforce discusses the impact of data quality on AI outcomes and
strategies to enhance data integrity in their documentation on AI and data management, which can
be referenced at Data Management for AI.
How does AI assist in lead qualification?
A
Explanation:
AI assists in lead qualification primarily by scoring leads based on customer data. This process,
known as lead scoring, uses machine learning algorithms to evaluate leads against a set of
predefined criteria that reflect potential interest and sales readiness. The scores assigned help sales
teams prioritize their efforts toward leads most likely to convert, thus improving efficiency and
success rates in sales activities. Salesforce AI enhances this process through features like Einstein
Lead Scoring, which automatically calculates scores based on both historical conversion data and
behavioral data from prospects. For further insights, Salesforce provides detailed documentation on
lead scoring with AI at Salesforce Einstein Lead Scoring.
Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a large amount
of data.
Which data quality element should the company focus on?
A
Explanation:
To improve the quality of AI model predictions, Cloud Kicks should focus on the accuracy of the data.
Accurate data ensures that the insights and predictions generated by AI models are reliable and
valid. Data accuracy involves correcting errors, filling missing values, and verifying data sources to
enhance the quality of information fed into the AI systems. Focusing on data accuracy helps in
minimizing prediction errors and enhances the decision-making process based on AI insights. For
more details on the importance of data quality in AI models, Salesforce provides extensive guidance
in their documentation, which can be found at Data Quality and AI.
Which action introduces bias in the training data used for AI algorithms?
C
Explanation:
Introducing bias in training data for AI algorithms occurs when the dataset used underrepresents
certain perspectives and populations. This type of bias can skew AI predictions, making the system
less fair and accurate. For example, if a dataset predominantly contains information from one
demographic group, the AI's performance may not generalize well to other groups, leading to biased
or unfair outcomes. Salesforce discusses the impact of biased training data and ways to mitigate this
in their AI ethics guidelines, which can be explored further in the Salesforce AI documentation on
Responsible Creation of AI.
What should an organization do to enforce consistency across accounts for newly entered records?
C
Explanation:
To ensure consistency across accounts for newly entered records, organizations should implement
naming conventions or a predefined list of user-selectable values. This approach standardizes data
entry, reducing variations and errors. It also helps in maintaining clean data which is essential for
accurate reporting and analytics. Using standardized naming conventions ensures that all users
adhere to a consistent format, making it easier to manage and analyze data across the organization.
For more information on best practices for data management in Salesforce, refer to Salesforce's
documentation on Data Management Best Practices.
Which best describes the difference between predictive AI and generative Al?
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.