This page was exported from Exams Labs Braindumps [ http://blog.examslabs.com ] Export date:Wed Apr 2 17:02:29 2025 / +0000 GMT ___________________________________________________ Title: Feb 18, 2025 Salesforce-AI-Associate Exam Crack Test Engine Dumps Training With 104 Questions [Q28-Q44] --------------------------------------------------- Feb 18, 2025 Salesforce-AI-Associate Exam Crack Test Engine Dumps Training With 104 Questions Obtain the Salesforce-AI-Associate PDF Dumps Get 100% Outcomes Exam Questions For You To Pass Salesforce Salesforce-AI-Associate Exam Syllabus Topics: TopicDetailsTopic 1AI Capabilities in CRM: Get familiar with the benefits of AI and capabilities of CRM.Topic 2Data for AI: Questions about the importance of data quality and different elements or components of data quality are related to this topic.Topic 3AI Fundamentals: This topic discusses the major principles and applications of AI within Salesforce. It also focuses on different types of AI and their capabilities.Topic 4Ethical Considerations of AI: It delves into the ethical challenges of AI such as human bias in machine learning, lack of transparency, etc. The topic also explains how to apply Trusted AI Principles of Salesforce to given scenarios.   Q28. 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.”Q29. What should an organization do to enforce consistency across accounts for newly entered records?  Merge all duplicate accounts into a single record when duplicate entries are detected.  Input the data exactly as it appears from the source, such as the company’s website or social media,  Implement naming conventions or a predefined list of user-selectable values for organization-wide records. 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.Q30. What is the significance of explainability of trusted AI systems?  Increases the complexity of AI models  Enhances the security and accuracy of AI models  Describes how Al models make decisions The significance of the explainability of trusted AI systems is that it describes how AI models make decisions.Explainability is crucial for building trust and accountability in AI systems, ensuring that users and stakeholders understand the decision-making processes and outcomes generated by AI. This is particularly important in scenarios where AI decisions impact personal or financial status, such as in credit scoring or healthcare diagnostics. Salesforce emphasizes the importance of explainable AI through its ethical AI practices, aiming to make AI systems more transparent and understandable. More details about Salesforce’s approach to ethical and explainable AI can be found in Salesforce AI ethics resources at Salesforce AI Ethics.Q31. 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. “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.”Q32. How does AI which CRM help sales representatives better understand previous customer interactions?  Creates, localizes, and translates product descriptions  Triggers personalized service replies  Provides call summaries “Providing call summaries is how AI with CRM helps sales representatives better understand previous customer interactions. Call summaries are a feature that uses natural language processing (NLP) to analyze voice conversations between sales representatives and customers and generate summaries or transcripts of the calls. Call summaries can help sales representatives better understand previous customer interactions by providing key information, insights, or action items from the calls.”Q33. 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?  Number of variables ipn the dataset  Size of the dataset  Age of the dataset “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 thefeasibility 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.”Q34. 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. “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.”Q35. 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.”Q36. 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 “Streamlining case management by categorizing and tracking customer support cases, identifying topics, and summarizing case resolutions are some key benefits of AI in improving customerexperiences 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.”Q37. What are the key components of the data quality standard?  Naming, formatting, Monitoring  Accuracy, Completeness, Consistency  Reviewing, Updating, Archiving 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.”Q38. Cloud Kicks relies on data analysis to optimize its product recommendation; however, CK encounters a recurring Issue of Incomplete customer records, withmissing contact Information and incomplete purchase histories.How will this incomplete data quality impact the company’s operations?  The response time for product recommendations is stalled.  The accuracy of product recommendations is hindered.  The diversity of product recommendations Is Improved. “The incomplete data quality will impact the company’s operations by hindering the accuracy of product recommendations. Incomplete data means that the data is missing some values or attributes that are relevant for the AI task. Incomplete data can affect the performance and reliability of AI models, as they may not have enough information to learn from or make accurate predictions. For example, incomplete customer records can affect the quality of product recommendations, as the AI model may not be able to capture the customers’ preferences, behavior, or needs.”Q39. 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.”Q40. Cloud Kicks wants to use AI to enhance its sales processesand customer support.Which capacity should they use?  Dashboard of Current Leads and Cases  Sales path and Automaton Case Escalations  Einstein Lead Scoring and Case Classification “Einstein Lead Scoring and Case Classification are thecapabilities that Cloud Kicks should use to enhance its sales processes and customer support. Einstein Lead Scoring and Case Classification are features that use AI tooptimize sales and service processes by providing insights and recommendations based ondata. 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.”Q41. What is a societal implication of excluding ethics in AI development?  Faster and cheaper development  More innovation and creativity  Harm to marginalized communities 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.Q42. What is a benefit of a diverse, balanced, and large dataset?  Training time  Data privacy  Model accuracy “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 orunderfitting 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.”Q43. What is the role of Salesforce Trust AI principles in the context of CRM system?  Guiding ethical and responsible use of AI  Providing a framework for AI data model accuracy  Outlining the technical specifications for AI integration “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 practicesfor 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.”Q44. What are the key components of the data quality standard?  Reviewing, Updating, Archiving  Naming, formatting, Monitoring  Accuracy, Completeness, Consistency 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.” Loading … Salesforce-AI-Associate Exam Dumps Contains FREE Real Quesions from the Actual Exam: https://www.examslabs.com/Salesforce/AI-Associate/best-Salesforce-AI-Associate-exam-dumps.html --------------------------------------------------- Images: https://blog.examslabs.com/wp-content/plugins/watu/loading.gif https://blog.examslabs.com/wp-content/plugins/watu/loading.gif --------------------------------------------------- --------------------------------------------------- Post date: 2025-02-18 10:45:03 Post date GMT: 2025-02-18 10:45:03 Post modified date: 2025-02-18 10:45:03 Post modified date GMT: 2025-02-18 10:45:03