Free Salesforce (Salesforce-AI-Associate) Certification Sample Questions with Online Practice Test [Q36-Q60]

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December 20, 2023

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Free Salesforce (Salesforce-AI-Associate) Certification Sample Questions with Online Practice Test

Salesforce-AI-Associate Certification Study Guide Pass Salesforce-AI-Associate Fast

QUESTION 36
A Salesforce administrator creates a new field to capture an order’s destination country.
Which field type should they use to ensure data quality?

Text

Picklist

Number

Explanation
“A picklist field type should be used to ensure data quality for capturing an order’s destination country. A picklist field type allows the user to select one or more predefined values from a list. A picklist field type can ensure data quality by enforcing consistency, accuracy, and completeness of the data values.”

QUESTION 37
How is natural language processing (NLP) used in the context of AI capabilities?

To cleanse and prepare data for AI implementations

To interpret and understand programming language

To understand and generate human language

Explanation
“Natural language processing (NLP) is used in the context of AI capabilities to understand and generate human language. NLP can enable AI systems to interact with humans using natural language, such as speech or text. NLP can also enable AI systems to analyze and extract information from natural language data, such as documents, emails, or social media posts.”

QUESTION 38
Cloud Kicks wants to develop a solution to predict customers product interests based on historical data. The company found that employees from one region use a text field to capture the product category, while employees from all other locations use a plckllst.
Which data quality dimension is affected in this scenario?

Completeness

Accuracy

Consistency

Explanation
“Consistency is the data quality dimension that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.
Inconsistent data can cause confusion, errors, or duplication in data analysis and processing. For example, using different field types for the same attribute can affect the consistency of the data.”

QUESTION 39
Which features of Einstein enhance sales efficiency and effectiveness?

Opportunity List View, Lead List View, Account List view

Opportunity Scoring, Opportunity List View, Opportunity Dashboard

Opportunity Scoring, Lead Scoring, Account Insights

Explanation
“Opportunity Scoring, Lead Scoring, Account Insights are features of Einstein that enhance sales efficiency and effectiveness. Opportunity Scoring and Lead Scoring use predictive models to assign scores to opportunities and leads based on their likelihood to close or convert. Account Insights use natural language processing (NLP) to provide relevant news and insights about accounts based on their industry, location, or events.”

QUESTION 40
Which type of bias imposes a system ‘s values on others?

Societal

Automation

Association

Explanation
“Societal bias is the type of bias that imposes a system’s values on others. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. Societal bias can affect the fairness and ethics of AI systems, as they may affect how different groups or domains are perceived, treated, or represented by AI systems. For example, societal bias can occur when AI systems impose a system’s values on others, such as using Western standards of beauty or success to judge or rank people from other cultures.”

QUESTION 41
What is an example of ethical debt?

Violating a data privacy law and falling to pay fines

Launching an AI feature after discovering a harmful bias

Delaying an AI product launch to retrain an AI data model

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.”

QUESTION 42
What is a Key consideration regarding data quality in AI implementation?

Techniques from customizing AI features in Salesforce

Data’s role in training and fine-tuning Salesforce AI models

Integration process of AI models with Salesforce workflows

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.”

QUESTION 43
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

Usage

Accuracy

Consistency

Explanation
“Consistency is the data quality dimension that is affected by multiple variations of state and country values in contact records. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Inconsistent data can cause confusion, errors, or duplication in data analysis and processing.”

QUESTION 44
What is a key challenge of human AI collaboration in decision-making?

Leads to move informed and balanced decision-making

Creates a reliance on AI, potentially leading to less critical thinking and oversight

Reduce the need for human involvement in decision-making processes

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.”

QUESTION 45
What is the main focus of the Accountability principle in Salesforce’s Trusted AI Principles?

Safeguarding fundamental human rights and protecting sensitive data

Taking responsibility for one’s actions toward customers, partners, and society

Ensuring transparency In Al-driven recommendations and predictions

Explanation
“The main focus of the Accountability principle in Salesforce’s Trusted AI Principles is taking responsibility for one’s actions toward customers, partners, and society. Accountability means that AI systems should be designed and developed with respect for the impact and consequences of their actions on others.
Accountability also means that AI developers and users should be aware of and adhere to the ethical, legal, and regulatory standards and expectations of their industry and domain.”

QUESTION 46
Cloud kicks wants to develop a solution to predict customers’ interest based on historical data. The company found that employee region uses a text field to capture the product category while employee from all other locations use a picklist.
Which dimension of data quality is affected in this scenario?

Accuracy

Consistency

Completeness

Explanation
“Consistency is the dimension of data quality that is affected in this scenario. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.
Inconsistent data can cause confusion, errors, or duplication in data analysis andprocessing. For example, using different field types for the same attribute can affect the consistency of the data.”

QUESTION 47
Cloud Kicks wants to create a custom service analytics application to analyze cases in Salesforce. The application should rely on accurate data to ensure efficient case resolution.
Which data quality dimension Is essential for this custom application?

Consistency

Duplication

Age

Explanation
“Consistency is the data quality dimension that is essential for creating a custom service analytics application to analyze cases in Salesforce. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources. Consistent data can ensure that the custom application can accurately and efficiently analyze cases and provide meaningful insights.”

QUESTION 48
What is the best method to safeguard customer data privacy?

Automatically anonymize all customer data.

Track customer data consent preferences.

Archive customer data on a recurring schedule.

Explanation
“Tracking customer data consent preferences is the best method to safeguard customer data privacy. Data privacy is the right of individuals to control how their personal data is collected, used, shared, or stored by others. Tracking customer data consent preferences means respecting and honoring the choices and preferences of customers regarding their personal data. Tracking customer data consent preferences can help ensure compliance with data privacy laws and regulations, as well as build trust and loyalty with customers.”

QUESTION 49
What is the key difference between generative and predictive AI?

Generative AI creates new content based on existing data and predictive AI analyzes existing data.

Generative AI finds content similar to existing data and predictive AI analyzes existing data.

Generative AI analyzes existing data and predictive AI creates new content based on existing data.

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.”

QUESTION 50
What is a benefit of a diverse, balanced, and large dataset?

Training time

Data privacy

Model accuracy

Explanation
“Model accuracy is a benefit of a diverse, balanced, and large dataset. A diverse dataset can capture a variety of features and patterns that are relevant for the AI task. A balanced dataset can avoid overfitting or underfitting the model to a specific subset of data. A large dataset can provide enough information for the model to learn from and generalize well to new data.”

QUESTION 51
What is a key characteristic of machine learning in the context of AI capabilities?

Uses algorithms to learn from data and make decisions

Relies on preprogrammed rules to make decisions

Can perfectly mimic human intelligence and decision-making

Explanation
“Machine learning is a key characteristic of AI capabilities that uses algorithms to learn from data and make decisions. Machine learning is a branch of AI that enables computers to learn from data without being explicitly programmed. Machine learning algorithms can analyze data, identify patterns, and make predictions or recommendations based on the data.”

QUESTION 52
What are the three commonly used examples of AI in CRM?

Predictive scoring, reporting, Image classification

Predictive scoring, forecasting, recommendations

Einstein Bots, face recognition, recommendations

Explanation
“Predictive scoring, forecasting, and recommendations are three commonly used examples of AI in CRM.
Predictive scoring can help prioritize leads, opportunities, and customers based on their likelihood to convert, churn, or buy. Forecasting can help predict future sales, revenue, or demand based on historical data and trends. Recommendations can help suggest the best products, services, or actions for each customer based on their preferences, behavior, and needs.”

QUESTION 53
What role does data quality play in the ethical us of AI applications?

High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi…

High-quality data ensures the process of demographic attributes requires for personalized campaigns.

Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.

Explanation
“High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data.”

QUESTION 54
Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results?
What to a potential mason for this?

Poor data quality

The wrong product

Too much data

Explanation
“Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task. Poor data quality can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions.”

QUESTION 55
A business analyst (BA) wants to improve business by enhancing their sales processes and customer..
Which AI application should the BA use to meet their needs?

Sales data cleansing and customer support data governance

Machine learning models and chatbot predictions

Lead scoring, opportunity forecasting, and case classification

Explanation
“Lead scoring, opportunity forecasting, and case classification are AI applications that can help a business analyst improve their sales processes and customer support. Lead scoring can help prioritize leads based on their likelihood to convert, opportunity forecasting can help predict future sales or revenue based on historical data and trends, and case classification can help categorize and route cases based on their attributes.”

QUESTION 56
What should organizations do to ensure data quality for their AI initiatives?

Collect and curate high-quality data from reliable sources.

Rely on AI algorithms to automatically handle data quality issues.

Prioritize model fine-tuning over data quality improvements.

Explanation
“Organizations should collect and curate high-quality data from reliable sources to ensure data quality for their AI initiatives. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. Reliable sources mean that the data is trustworthy, credible, and authoritative. Collecting and curating high-quality data from reliable sources can improve the performance and reliability of AI systems.”

QUESTION 57
Salesforce defines bias as using a person’s Immutable traits to classify them or market to them.
Which potentially sensitive attribute is an example of an immutable trait?

Financial status

Nickname

Email address

Explanation
“Financial status is an example of an immutable trait. Immutable traits are characteristics that are inherent, fixed, or unchangeable. For example, financial status is an immutable trait because it is determined by factors beyond one’s control, such as birth, inheritance, or economic conditions. Nickname and email address are not immutable traits because they can be changed by choice or preference.”

QUESTION 58
What is a potential outcome of using poor-quality data in AI application?

AI model training becomes slower and less efficient

AI models may produce biased or erroneous results.

AI models become more interpretable

Explanation
“A potential outcome of using poor-quality data in AI applications is that AI models may produce biased or erroneous results. Poor-quality data means that the data is inaccurate, incomplete,inconsistent, irrelevant, or outdated for the AI task. Poor-quality data can affect the performance and reliability of AI models, as they may not have enough or correct information to learn from or make accurate predictions. Poor-quality data can also introduce or exacerbate biases or errors in AI models, such as human bias, societal bias, confirmation bias, or overfitting or underfitting.”

QUESTION 59
What is a sensitive variable that car esc to bias?

Education level

Country

Gender

Explanation
“Gender is a sensitive variable that can lead to bias. A sensitive variable is a variable that can potentially cause discrimination or unfair treatment based on a person’s identity or characteristics. For example, gender is a sensitive variable because it can affect how people are perceived, treated, or represented by AI systems.”

QUESTION 60
How does the “right of least privilege” reduce the risk of handling sensitive personal data?

By limiting how many people have access to data

By reducing how many attributes are collected

By applying data retention policies

Explanation
“The “right of least privilege” reduces the risk of handling sensitive personal data by limiting how many people have access to data. The “right of least privilege” is a security principle that states that each user or system should have the minimum level of access or privilege necessary to perform their tasks or functions.
The “right of least privilege” can help protect sensitive personal data from unauthorized access, misuse, or leakage.”

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