Question # 1 Which Einstein capability uses emails to create content for Knowledge articles? A. GenerateB. DiscoverC. Predict
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A. Generate
Answer Description Explanation: “Einstein Generate uses emails to create content for Knowledge articles. Einstein Generate is a natural language generation (NLG) feature that can automatically write summaries, descriptions, or recommendations based on data or text inputs. For example, Einstein Generate can analyze email conversations between agents and customers and generate draft articles for the Knowledge base.”
Question # 2 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.
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A. Empower users to off all skill level to build AI application with clicks, not code.
Answer Description 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.”
Question # 3 Which action should be taken to develop and implement trusted generated AI with Salesforce’s safety guideline in mind? A. Develop right-sized models to reduce our carbon footprint.B. Create guardrails that mitigates toxicity and protect PIIC. Be transparent when AI has created and automatically delivered content.
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B. Create guardrails that mitigates toxicity and protect PII
Answer Description Explanation: “Creating guardrails that mitigate toxicity and protect PII is an action that should be taken to develop and implement trusted generative AI with Salesforce’s safety guideline in mind. Salesforce’s safety guideline is one of the Trusted AI Principles that states that AI systems should be designed and developed with respect for the safety and well-being of humans and the environment. Creating guardrails means implementing measures or mechanisms that can prevent or limit the potential harm or risk caused by AI systems. For example, creating guardrails can help mitigate toxicity by filtering out inappropriate or offensive content generated by AI systems. Creating guardrails can also help protect PII by masking or anonymizing personal or sensitive information generated by AI systems.”
Question # 4 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.
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B. Biases in data can be inadvertently learned and amplified by AI systems.
Answer Description Explanation: “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 # 5 What is an example of ethical debt? A. Violating a data privacy law and failing to pay finesB. Delaying an AI product launch to retrain an AI data modelC. Launching an AI feature after discovering a harmful bias
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C. Launching an AI feature after discovering a harmful biasQuestion # 6 What are the key components of the data quality standard? A. Naming, formatting, MonitoringB. Accuracy, Completeness, ConsistencyC. Reviewing, Updating, Archiving
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B. Accuracy, Completeness, Consistency
Answer Description 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.
Question # 7 What are the potential consequences of an organization suffering from poor data quality? A. Low employee morale, stock devaluation, and inability to attract top talentB. Revenue loss, poor customer service, and reputational damageC. Technical debt, monolithic system architecture, and slow ETL throughput
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B. Revenue loss, poor customer service, and reputational damage
Answer Description Explanation: The potential consequences of an organization suffering from poor data quality include revenue loss, poor customer service, and reputational damage. Poor data quality can lead to inaccurate analytics and decision-making, impacting customer interactions, marketing strategies, and financial forecasting. These issues ultimately affect customer satisfaction and could lead to financial losses and a damaged brand reputation. Salesforce highlights the importance of maintaining high data quality for effective CRM and AI applications, offering various tools and best practices to enhance data integrity. For guidance on managing and improving data quality in Salesforce, see the Salesforce documentation on data quality at Salesforce Data Quality.
Question # 8 What is a Key consideration regarding data quality in AI implementation? A. Techniques from customizing AI features in SalesforceB. Data’s role in training and fine-tuning Salesforce AI modelsC. Integration process of AI models with Salesforce workflows
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B. Data’s role in training and fine-tuning Salesforce AI models
Answer Description Explanation: “Data’s role in training and fine-tuning Salesforce AI models is a key consideration regarding data quality in AI implementation. 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’s role in training and fine-tuning Salesforce AI models means understanding how data is used to build, train, test, and improve AI models in Salesforce, such as Einstein Prediction Builder or Einstein Discovery.”
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