Question # 1
| How does an organization benefit from using AI to personalize the shopping experience of online customers? | | A. Customers are more likely to share personal information with a site that personalizes their experience. | | B. Customers are more likely to be satisfied with their shopping experience. | | C. Customers are more likely to visit competitor sites that personalize their experience. |
B. Customers are more likely to be satisfied with their shopping experience.
Explanation: “An organization benefits from using AI to personalize the shopping experience of online customers by increasing customer satisfaction. AI can help provide customized and relevant product recommendations, offers, or content based on the customers’ preferences, behavior, or needs. AI can also help create a more engaging and interactive shopping experience by using natural language processing (NLP) or computer vision techniques. Personalized shopping experiences can improve customer satisfaction by meeting their expectations, needs, and interests.”
Question # 2
| 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? | | A. Reliability | | B. Volume | | C. Age |
A. Reliability
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.”
Question # 3
| Cloud Kicks is testing a new AI model. Which approach aligns with Salesforce's Trusted AI Principle of Inclusivity? | | A. Test only with data from a specific region or demographic to limit the risk of data leaks. | | B. Rely on a development team with uniform backgrounds to assess the potential societal implications of the model. | | C. Test with diverse and representative datasets appropriate for how the model will be used. |
C. 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.
Question # 4
| What is the key difference between generative and predictive AI? | | A. Generative AI creates new content based on existing data and predictive AI analyzes existing data. | | B. Generative AI finds content similar to existing data and predictive AI analyzes existing data. | | C. Generative AI analyzes existing data and predictive AI creates new content based on existing data. |
A. Generative AI creates new content based on existing data and predictive AI analyzes existing data.
Explanation:
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.”
Question # 5
| What is the rile of data quality in achieving AI business Objectives? | | A. Data quality is unnecessary because AI can work with all data types. | | B. Data quality is required to create accurate AI data insights. | | C. Data quality is important for maintain Ai data storage limits |
B. Data quality is required to create accurate AI data insights.
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.”
Question # 6
| What is the significance of explainability of trusted AI systems? | | A. Increases the complexity of AI models | | B. Enhances the security and accuracy of AI models | | C. Describes how Al models make decisions |
C. Describes how Al models make decisions
Explanation: 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.
Question # 7
| Cloud Kicks wants to improve the quality of its AI model's predictions with the use of a large amount of data.
Which data quality element should the company focus on? | | A. Accuracy | | B. Location | | C. Volume |
A. Accuracy
Explanation: To improve the quality of AI model predictions, Cloud Kicks should focus on the accuracy of the data. Accurate data ensures that the insights and predictions generated by AI models are reliable and valid. Data accuracy involves correcting errors, filling missing values, and verifying data sources to enhance the quality of information fed into the AI systems. Focusing on data accuracy helps in minimizing prediction errors and enhances the decision-making process based on AI insights. For more details on the importance of data quality in AI models, Salesforce provides extensive guidance in their documentation, which can be found at Data Quality and AI.
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