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  • [2023] Salesforce-AI-Associate Answers Salesforce-AI-Associate Free Demo Are Based On The Real Exam [Q24-Q44]

[2023] Salesforce-AI-Associate Answers Salesforce-AI-Associate Free Demo Are Based On The Real Exam [Q24-Q44]

Posted on November 11, 2023 By freedumps No Comments on [2023] Salesforce-AI-Associate Answers Salesforce-AI-Associate Free Demo Are Based On The Real Exam [Q24-Q44]
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[2023] Salesforce-AI-Associate Answers Salesforce-AI-Associate Free Demo Are Based On The Real Exam

Salesforce-AI-Associate [Nov-2023 Newly Released] Exam Questions For You To Pass

Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic Details
Topic 1
  • Describe the importance of data quality
  • Explain the basic principles and applications of AI within Salesforce
Topic 2
  • AI Capabilities in CRM
  • Ethical Considerations of AI
  • AI Fundamentals
Topic 3
  • Identify CRM AI capabilities
  • Differentiate between the types of AI and their capabilities
Topic 4
  • Apply Salesforce’s Trusted AI Principles to given scenarios
  • Describe the benefits of AI as they apply to CRM
Topic 5
  • Describe the ethical challenges of AI
  • Describe the elements
  • components of data quality

 

NEW QUESTION 24
What role does data quality play in the ethical us of AI applications?

 
 
 
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.”

NEW QUESTION 25
What should be done to prevent bias from entering an AI system when training it?

 
 
 
Explanation
“Using diverse training data is what should be done to prevent bias from entering an AI system when training it. Diverse training data means that the data covers a wide range of features andpatterns that are relevant for the AI task. Diverse training data can help prevent bias by ensuring that the AI system learns from a balanced and representative sample of the target population or domain. Diverse training data can also help improve the accuracy and generalization of the AI system by capturing more variations and scenarios in the data.”

NEW QUESTION 26
What is the key difference between generative and predictive AI?

 
 
 
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 27
Cloud Kicks discovered multiple variations of state and country values in contact records.
Which data quality dimension is affected by this issue?

 
 
 
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.”

NEW QUESTION 28
The Cloud technical team is assessing the effectiveness of their AI development processes?
Which established Salesforce Ethical Maturity Model should the team use to guide the development of trusted AI solution?

 
 
 
Explanation
“The Ethical AI Process Maturity Model is the established Salesforce Ethical Maturity Model that the Cloud technical team should use to guide the development of trusted AI solutions. The Ethical AI Process Maturity Model is a framework that helps assess and improve the ethical and responsible practices and processes involved in developing and deploying AI systems. The Ethical AI Process Maturity Model consists of five levels of maturity: Ad Hoc, Aware, Defined, Managed, and Optimized. The Ethical AI Process Maturity Model can help guide the development of trusted AI solutions by providing a roadmap and best practices for achieving higher levels of ethical maturity.”

NEW QUESTION 29
What is an example of ethical debt?

 
 
 
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.”

NEW QUESTION 30
Cloud Kicks learns of complaints from customers who are receiving too many sales calls and emails.
Which data quality dimension should be assessed to reduce these communication Inefficiencies?

 
 
 
Explanation
“Duplication is the data quality dimension that should be assessed to reduce communication inefficiencies.
Duplication means that the data contains multiple copies or instances of the same record or value. Duplication can cause confusion, errors, or waste in data analysis and processing. For example, duplication can lead to communication inefficiencies if customers receive multiple calls or emails from different sources for the same purpose.”

NEW QUESTION 31
What are the key components of the data quality standard?

 
 
 
Explanation
“Accuracy, Completeness, Consistency are the key components of the data quality standard. Data quality standard is a set of criteria or measures that define and evaluate the quality of data for a specific purpose or task. Data quality standard can vary by industry, domain, or application, but some common components are accuracy, completeness, and consistency. Accuracy means that the data values are correct and valid for the data attribute. Completeness means that the data values are not missing any relevant information for the data attribute. Consistency means that the data values are uniform and follow a common standard or format across different records, fields, or sources.”

NEW QUESTION 32
Cloud Kicks wants to ensure that multiple records for the same customer are removed in Salesforce.
Which feature should be used to accomplish this?

 
 
 
Explanation
“Duplicate management should be used to remove multiple records for the same customer in Salesforce.
Duplicate management is a feature that helps prevent and manage duplicate records in Salesforce. Duplicate management can help define matching rules, duplicate rules, and alert messages to detect and merge duplicate records.”

NEW QUESTION 33
What is a potential source of bias in training data for AI models?

 
 
 
Explanation
“A potential source of bias in training data for AI models is that the data is skewed toward a particular demographic or source. Skewed data means that the data is not balanced or representative of the target population or domain. Skewed data can introduce or exacerbate bias in AI models, as they may overfit or underfit the model to a specific subset of data. For example, skewed data can lead to bias if the data is collected from a limited or biased demographic or source, such as a certain age group, gender, race, location, or platform.”

NEW QUESTION 34
Which type of bias results from data being labeled according to stereotypes?

 
 
 
Explanation
“Societal bias results from data being labeled according to stereotypes. Societal bias is a type of bias that reflects the assumptions, norms, or values of a specific society or culture. For example, societal bias can occur when data is labeled based on gender, race, ethnicity, or religion stereotypes.”

NEW QUESTION 35
Cloud Kicks implements a new product recommendation feature for its shoppers that recommends shoes of a given color to display to customers based on the color of the products from their purchase history.
Which type of bias is most likely to be encountered in this scenario?

 
 
 
Explanation
“Confirmation bias is most likely to be encountered in this scenario. Confirmation bias is a type of bias that occurs when data or information confirms or supports one’s existing beliefs or expectations. For example, confirmation bias can occur when a product recommendation feature only recommends shoes of a given color based on the customer’s purchase history, without considering other factors or preferences that may influence their choice.”

NEW QUESTION 36
A financial institution plans a campaign for preapproved credit cards?
How should they implement Salesforce’s Trusted AI Principle of Transparency?

 
 
 
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 37
How does a data quality assessment impact business outcome for companies using AI?

 
 
 
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 38
What are the three commonly used examples of AI in CRM?

 
 
 
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.”

NEW QUESTION 39
Cloud Kicks wants to use AI to enhance its sales processes and customer support.
Which capacity should they use?

 
 
 
Explanation
“Einstein Lead Scoring and Case Classification are the capabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI to optimize sales and service processes by providing insights and recommendations based on data. Einstein Lead Scoring can help prioritize leads based on their likelihood to convert, while Einstein Case Classification can help categorize and route cases based on their attributes.”

NEW QUESTION 40
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?

 
 
 
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.”

NEW QUESTION 41
A developer is tasked with selecting a suitable dataset for training an AI model in Salesforce to accurately predict current customer behavior.
What Is a crucial factor that the developer should consider during selection?

 
 
 
Explanation
“The size of the dataset is a crucial factor that the developer should consider during selection. The size of the dataset refers to the amount or volume of data available for training an AI model. The size of the dataset can affect the feasibility and quality of the AI model, as well as the choice of AI techniques and tools. The size of the dataset should be large enough to provide sufficient information for the AI model to learn from and generalize well to new data.”

NEW QUESTION 42
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?

 
 
 
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.”

NEW QUESTION 43
What is the role of Salesforce Trust AI principles in the context of CRM system?

 
 
 
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 practices for 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 44
What is a potential outcome of using poor-quality data in AI application?

 
 
 
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.”

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