Learn why reliability is an essential data quality dimension for AI models to predict the demand for shoes using historical data and Salesforce.
Table of Contents
Question
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. Agew
Answer
A. Reliability
Explanation
The correct answer is A. Reliability. Reliability is the degree to which data is accurate, consistent, and free of errors or anomalies. Reliable data is essential for AI models to make accurate predictions based on historical data. If the data is unreliable, the AI model may produce inaccurate or misleading results that could affect the demand forecasting for shoes. Volume and age are not data quality dimensions, but rather data characteristics that may affect the performance or relevance of the AI model.
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.
The latest Salesforce AI Associate actual real practice exam question and answer (Q&A) dumps are available free, helpful to pass the Salesforce AI Associate certificate exam and earn Salesforce AI Associate certification.