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2024 Realistic Salesforce-AI-Associate 100% Pass Guaranteed Download Exam Q&A [Q43-Q59]

Published date January 9, 2024

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2024 Realistic Salesforce-AI-Associate 100% Pass Guaranteed Download Exam Q&A

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Salesforce Salesforce-AI-Associate Exam Syllabus Topics:

Topic	Details
Topic 1	Apply Salesforce’s Trusted AI Principles to given scenarios Describe the benefits of AI as they apply to CRM
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	Describe the importance of data quality Explain the basic principles and applications of AI within Salesforce
Topic 5	Describe the ethical challenges of AI Describe the elements components of data quality

Q43. 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.”

Q44. 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.”

Q45. Cloud Kicks wants to use an AI mode to predict the demand for shoes using historical data on sales and regional characteristics.
What is an essential data quality dimension to achieve this goal?

Reliability

Volume

Age

Explanation
“Reliability is an essential data quality dimension to achieve the goal of predicting the demand for shoes using historical data on sales and regional characteristics. Reliability means that the data values are trustworthy, credible, and authoritative for the AI task. Reliable data can improve the accuracy and confidence of AI predictions, as they reflect the true state or condition of the target population or domain. For example, reliable data can help predict the demand for shoes by using verified and validated sales and regional data.”

Q46. What are some key benefits of AI in improving customer experiences in CRM?

Improves CRM security protocols, safeguarding sensitive customer data from potential breaches and threats

Streamlines case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions

Fully automates the customer service experience, ensuring seamless automated interactions with customers

Explanation
“Streamlining case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions are some key benefits of AI in improving customer experiences in CRM. AI can help automate and optimize various aspects of customer service, such as routing cases to the right agents, providing relevant information or suggestions, and generating reports or insights. AI can also help enhance customer satisfaction and loyalty by reducing wait times, improving response quality, and providing personalized solutions.”

Q47. 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.”

Q48. How does a data quality assessment impact business outcome for companies using AI?

Improves the speed of AI recommendations

Accelerates the delivery of new AI solutions

Provides a benchmark for AI predictions

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.”

Q49. What is the rile of data quality in achieving AI business Objectives?

Data quality is unnecessary because AI can work with all data types.

Data quality is required to create accurate AI data insights.

Data quality is important for maintain Ai data storage limits

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.”

Q50. A service leader wants use AI to help customer resolve their issues quicker in a guided self-serve application.
Which Einstein functionality provides the best solution?

Case Classification

Bots

Recommendation

Explanation
“Bots provide the best solution for a service leader who wants to use AI to help customers resolve their issues quicker in a guided self-serve application. Bots are a feature that uses natural language processing (NLP) and natural language understanding (NLU) to create conversational interfaces that can interact with customers using text or voice. Bots can help automate and streamline customer service processes by providing answers, suggestions, or actions based on the customer’s intent and context.”

Q51. Cloud Kicks is testing a new AI model.
Which approach aligns with Salesforce’s Trusted AI Principle of Incluslvity?

Test only with data from a specific region or demographic to limit the risk of data leaks.

Rely on a development team with uniform backgrounds to assess the potential societal implications of the model.

Test with diverse and representative datasets appropriate for how the model will be used.

Explanation
“Testing with diverse and representative datasets appropriate for how the model will be used aligns with Salesforce’s Trusted AI Principle of Inclusivity. Inclusivity means that AI systems should be designed and developed with respect for diversity and inclusion of different perspectives, backgrounds, and experiences.
Testing with diverse and representative datasets can help ensure that the models are fair, unbiased, and representative of the target population or domain.”

Q52. What is an implication of user consent in regard to AI data privacy?

AI ensures complete data privacy by automatically obtaining user consent.

AI infringes on privacy when user consent is not obtained.

AI operates Independently of user privacy and consent.

Explanation
“AI infringes on privacy when user consent is not obtained. User consent is the permission or agreement given by a user to allow their personal data to be collected, used, shared, or stored by others. User consent is an important aspect of data privacy, which is the right of individuals to control how their personal data is handled by others. AI infringes on privacy when user consent is not obtained because it violates the user’s rights and preferences regarding their personal data.”

Q53. Cloud Kicks wants to implement AI features on its 5aiesforce Platform but has concerns about potential ethical and privacy challenges.
What should they consider doing to minimize potential AI bias?

Integrate AI models that auto-correct biased data.

Implement Salesforce’s Trusted AI Principles.

Use demographic data to identify minority groups.

Explanation
“Implementing Salesforce’s Trusted AI Principles is what Cloud Kicks should consider doing to minimize potential AI bias. Salesforce’s Trusted 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.”

Q54. 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.”

Q55. Cloud Kicks wants to use Einstein Prediction Builder to determine a customer’s likelihood of buying specific products; however, data quality is a…
How can data quality be assessed quality?

Build a Data Management Strategy.

Build reports to expire the data quality.

Leverage data quality apps from AppExchange

Explanation
“Leveraging data quality apps from AppExchange is how data quality can be assessed. 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. Leveraging data quality apps from AppExchange means using third-party applications or solutions that can help measure, monitor, or improve data quality in Salesforce.”

Q56. 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.”

Q57. What is a potential source of bias in training data for AI models?

The data is collected in area time from sources systems.

The data is skewed toward is particular demographic or source.

The data is collected from a diverse range of sources and demographics.

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.”

Q58. 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?

Ethical AI Prediction Maturity Model

Ethical AI Process Maturity Model

Ethical AI practice Maturity Model

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.”

Q59. To avoid introducing unintended bias to an AI model, which type of data should be omitted?

Transactional

Engagement

Demographic

Explanation
“Demographic data should be omitted to avoid introducing unintended bias to an AI model. 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 ethics of AI systems.”

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