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?

1. A. Build a Data Management Strategy.
2. B. Build reports to expire the data quality.
3. C. Leverage data quality apps from AppExchange

Correct Answer: C
“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.”

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?

1. A. Accuracy
2. B. Location
3. C. Volume

Correct Answer: A
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.

What is the rile of data quality in achieving AI business Objectives?

1. A. Data quality is unnecessary because AI can work with all data types.
2. B. Data quality is required to create accurate AI data insights.
3. C. Data quality is important for maintain Ai data storage limits

Correct Answer: B
“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.”

What is Salesforce's Trusted AI Principle of Transparency?

1. A. The customization of AT features to meet specific business requirements
2. B. The integration of AT models with Salesforce workflows
3. C. The clear and understandable explanation of Al decisions and actions

Correct Answer: C
Salesforce's Trusted AI Principle of Transparency emphasizes the importance of providing clear and understandable explanations of AI decisions and actions. This principle ensures that users can understand how AI conclusions are drawn, which is crucial for trust and accountability, especially in business applications where AI decisions can have significant impacts. Transparency helps mitigate the "black box" nature of AI systems by making them more interpretable and allows for better oversight, compliance, and alignment with ethical guidelines. Salesforce elaborates on these principles in their ethical AI practices, which can be further explored at Salesforce Ethical AI.

What role does data quality play in the ethical us of AI applications?

1. A. High-quality data is essential for ensuring unbased and for fair AI decisions, promoting ethical use, and preventing discrimi…
2. B. High-quality data ensures the process of demographic attributes requires for personalized campaigns.
3. C. Low-quality data reduces the risk of unintended bias as the data is not overfitted to demographic groups.

Correct Answer: A
“High-quality data is essential for ensuring unbiased and fair AI decisions, promoting ethical use, and preventing discrimination. High-quality data means that the data is accurate, complete, consistent, relevant, and timely for the AI task. High-quality data can help ensure unbiased and fair AI decisions by providing a balanced and representative sample of the target population or domain. High-quality data can also help promote ethical use and prevent discrimination by respecting the rights and preferences of users regarding their personal data.”