Salesforce Salesforce Certifications SALESFORCE-AI-ASSOCIATE Questions & Answers

• Question 81:

  Cloud Kicks relies on data analysis to optimize its product recommendations for customers.

  How will incomplete data quality impact the company's recommendations?

  A. The response time for the product

  B. The accuracy of the product

  C. The diversity of the product

  Correct Answer: B

  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.

• Question 82:

  A sales manager wants to improve their processes using AI in Salesforce?

  Which application of AI would be most beneficial?

  A. Lead soring and opportunity forecasting

  B. Sales dashboards and reporting

  C. Data modeling and management

  Correct Answer: A

  "Lead scoring and opportunity forecasting are applications of AI that would be most beneficial for a sales manager who wants to improve their processes using AI in Salesforce. Lead scoring can help prioritize leads based on their likelihood to convert, while opportunity forecasting can help predict future sales or revenue based on historical data and trends. These applications of AI can help optimize sales processes by providing insights and recommendations that can increase sales efficiency and effectiveness."

• Question 83:

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

  A. AI ensures complete data privacy by automatically obtaining user consent.

  B. AI infringes on privacy when user consent is not obtained.

  C. AI operates Independently of user privacy and consent.

  Correct Answer: B

  "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."

• Question 84:

  A developer has a large amount of data, but it is scattered across different systems and is not standardized.

  Which key data quality element should they focus on to ensure the effectiveness of the AI models?

  A. Performance

  B. Consistency

  C. Volume

  Correct Answer: B

  When data is scattered and not standardized, the key data quality element a developer should focus on is consistency. Consistency refers to the uniformity and standardization of data across different systems, which is crucial for integrating and analyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforce provides tools and practices for ensuring data consistency, such as data integration and management solutions that help standardize and synchronize data across platforms. For more information on Salesforce data management, refer to the Salesforce data management tools at Salesforce Data Management.

• Question 85:

  What can bias in AI algorithms in CRM lead to?

  A. Personalization and target marketing changes

  B. Advertising cost increases

  C. Ethical challenges in CRM systems

  Correct Answer: C

  "Bias in AI algorithms in CRM can lead to ethical challenges in CRM systems. Bias means that AI algorithms favor or discriminate certain groups or outcomes based on irrelevant or unfair criteria. Bias can affect the fairness and ethics of CRM systems, as they may affect how customers are perceived, treated, or represented by AI algorithms. For example, bias can lead to ethical challenges in CRM systems if AI algorithms make inaccurate or harmful predictions or recommendations based on customers' identity or characteristics."

• Question 86:

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

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

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

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

  Correct Answer: B

  "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."

• Question 87:

  What is the most likely impact that high-quality data will have on customer relationships?

  A. Increased brand loyalty

  B. Higher customer acquisition costs

  C. Improved customer trust and satisfaction

  Correct Answer: C

  "The most likely impact that high-quality data will have on customer relationships is improved customer trust and satisfaction. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can improve customer relationships by enabling AI systems to provide personalized and relevant products, services, or solutions that meet the customers' expectations, needs, and interests. High-quality data can also improve customer trust and satisfaction by reducing errors, delays, or waste in customer interactions."

• Question 88:

  What is a possible outcome of poor data quality?

  A. AI models maintain accuracy but have slower response times.

  B. Biases in data can be inadvertently learned and amplified by AI systems.

  C. AI predictions become more focused and less robust.

  Correct Answer: B

  "A possible outcome of poor data quality is that biases in data can be inadvertently learned and amplified by AI systems. 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 systems, as they may not have enough or correct information to learn from or make accurate predictions. Poor data quality can also introduce or exacerbate biases in data, such as human bias, societal bias, or confirmation bias, which can affect the fairness and ethics of AI systems."

• Question 89:

  Which features of Einstein enhance sales efficiency and effectiveness?

  A. Opportunity List View, Lead List View, Account List view

  B. Opportunity Scoring, Opportunity List View, Opportunity Dashboard

  C. Opportunity Scoring, Lead Scoring, Account Insights

  Correct Answer: C

  "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 90:

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

  A. Predictive scoring, reporting, Image classification

  B. Predictive scoring, forecasting, recommendations

  C. Einstein Bots, face recognition, recommendations

  Correct Answer: B

  "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."