Learn how poor data quality can cause inaccurate results when using Einstein to generate predictions, and how to improve data quality with best practices.
Table of Contents
Question
Cloud Kicks uses Einstein to generate predictions out is not seeing accurate results. What to a potential mason for this?
A. Poor data quality
B. The wrong product
C. Too much data
Answer
A. Poor data quality
Explanation
The correct answer is A. Poor data quality. Data quality is a crucial factor that affects the accuracy of Einstein predictions. Data quality refers to how well the data reflects the reality of the business problem, how complete and consistent the data is, and how free the data is from errors and outliers. Poor data quality can lead to inaccurate or misleading predictions, as the model learns from unreliable or irrelevant information. To improve data quality, Cloud Kicks should follow the best practices for preparing data for Einstein, such as cleaning, filtering, and transforming the data, and ensuring that the data has enough variability and diversity.
Poor data quality is a potential reason for not seeing accurate results from an AI model. Poor data quality means that the data is inaccurate, incomplete, inconsistent, irrelevant, or outdated for the AI task.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 models, as they may not have enough or correct information to learn from or make accurate predictions.
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.