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
Answer: B Explanation: When data is scattered and not standardized, the key data quality element adeveloper should focus on is consistency. Consistency refers to the uniformity andstandardization of data across different systems, which is crucial for integrating andanalyzing data effectively, especially when developing AI models. Inconsistent data can lead to errors in analysis, poor AI model performance, and misleading insights. Salesforceprovides tools and practices for ensuring data consistency, such as data integration andmanagement solutions that help standardize and synchronize data across platforms. Formore information on Salesforce data management, refer to the Salesforce datamanagement tools at Salesforce Data Management.
Question # 22
What are predictive analytics, machine learning, natural language processing (NLP), and computer vision?
A. Different types of data models used in Salesforce B. Different types of automation tools used in Salesforce C. Different types of AI that can be applied in Salesforce
Answer: C Explanation: Predictive analytics, machine learning, natural language processing (NLP),and computer vision are all types of artificial intelligence technologies that can be applied inSalesforce to enhance various aspects of business operations and customer interactions.Predictive analytics uses historical data to make predictions about future events. Machinelearning involves algorithms that can learn from and make decisions based on data. NLP isconcerned with the interactions between computers and humans using natural language,and computer vision interprets and processes visual information from the world to makesense of it in the way humans do. Salesforce harnesses these AI technologies, particularlythrough its Einstein platform, to provide powerful tools that help businesses automatetasks, make better decisions, and offer more personalized services. For more on howSalesforce utilizes these AI technologies, you can explore the Einstein AI servicesdocumentation at Salesforce Einstein.
Question # 23
What does the term "data completeness" refer to in the context of data quality?
A. The degree to which all required data points are present in the dataset B. The process of aggregating multiple datasets from various databases C. The ability to access data from multiple sources in real time
Answer: AExplanation: Data completeness is a measure of data quality that assesses whether all required data points are present in a dataset. It checks for missing values or gaps in datanecessary for accurate analysis and decision-making. In the context of Salesforce,ensuring data completeness is crucial for the effectiveness of CRM operations, reporting,and AI-driven applications like Salesforce Einstein, which rely on complete data to functionoptimally. Salesforce provides various tools and features, such as data validation rules andbatch data import processes, that help maintain data completeness across its platform.Detailed guidance on managing data quality in Salesforce can be found in the SalesforceHelp documentation on data management at Salesforce Help Data Management.
Question # 24
What is one technique to mitigate bias and ensure fairness in AI applications?
A. Ongoing auditing and monitoring of data that is used in AI applications B. Excluding data features from the Al application to benefit a population C. Using data that contains more examples of minority groups than majority groups
Answer: A Explanation: A technique to mitigate bias and ensure fairness in AI applications is ongoingauditing and monitoring of the data used in AI applications. Regular audits help identify andaddress any biases that may exist in the data, ensuring that AI models function fairly andwithout prejudice. Monitoring involves continuously checking the performance of AIsystems to safeguard against discriminatory outcomes. Salesforce emphasizes theimportance of ethical AI practices, including transparency and fairness, which can befurther explored through Salesforce’s AI ethics guidelines at Salesforce AI Ethics.
Question # 25
What are the potential consequences of an organization suffering from poor data quality?
A. Low employee morale, stock devaluation, and inability to attract top talent B. Revenue loss, poor customer service, and reputational damage C. Technical debt, monolithic system architecture, and slow ETL throughput
Answer: B Explanation: The potential consequences of an organization suffering from poor dataquality include revenue loss, poor customer service, and reputational damage. Poor dataquality can lead to inaccurate analytics and decision-making, impacting customerinteractions, marketing strategies, and financial forecasting. These issues ultimately affectcustomer satisfaction and could lead to financial losses and a damaged brand reputation.Salesforce highlights the importance of maintaining high data quality for effective CRM andAI applications, offering various tools and best practices to enhance data integrity. Forguidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
Question # 26
What is the main focus of the Accountability principle in Salesforce's Trusted AI Principles?
A. Safeguarding fundamental human rights and protecting sensitive data B. Taking responsibility for one's actions toward customers, partners, and society C. Ensuring transparency In Al-driven recommendations and predictions
Answer: B 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 # 27
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
Answer: C 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 # 28
What is a key benefit of effective interaction between humans and AI systems?
A. Leads to more informed and balanced decision making B. Alerts humans to the presence of biased data C. Reduces the need for human involvement
Answer: A Explanation“A key benefit of effective interaction between humans and AI systems is that it leads to more informed and balanced decision making. Effective interaction means that humans and AI systems can communicate and collaborate with each other in a clear, natural, and respectful way. Effective interaction can help leverage the strengths and complement the weaknesses of both humans and AI systems. Effective interaction can also help increase trust, confidence, and satisfaction in using AI systems.”
Question # 29
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?
A. Sales data cleansing and customer support data governance B. Machine learning models and chatbot predictions C. Lead scoring, opportunity forecasting, and case classification
Answer: C 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 # 30
In the context of Salesforce’s Trusted AI Principles what does the principle of Empowerment primarily aim to achieve?
A. Empower users to off all skill level to build AI application with clicks, not code. B. Empower users to contribute to the growing body of knowledge of leading AI research. C. Empower users to solve challenging technical problems using neural networks.
Answer: A Explanation“The principle of Empowerment primarily aims to achieve empowering users of all skill levels to build AI applications with clicks, not code. Empowerment is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the empowerment and education of humans. Empowering users means enabling users to access, use, and benefit from AI systems regardless of their technical expertise or background. For example, empowering users means providing tools and platforms that allow users to build AI applications with clicks, not code, such as Einstein Prediction Builder or Einstein Discovery.”