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Top 25 AI-900 Exam Questions and Answers Part-1. [image] Microsoft CERTIFIED AZURE A1 FUNDAMENTALS.
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[Audio] In order to make progress in machine learning, it is essential to carefully consider the process of splitting data for training and evaluation. There are various methods that can be used, but it is crucial to understand the most effective approach. Option A suggests using features for training and labels for evaluation, while option B proposes randomly splitting the data into rows for training and rows for evaluation. On the other hand, option C recommends using labels for training and features for evaluation, and option D suggests randomly splitting the data into columns for training and columns for evaluation. Considering the data and the specific needs of your project is crucial in determining the best method for splitting the data. The correct answer for this question is option B, as it allows for a more diverse set of data for both training and evaluation, resulting in a more accurate outcome. It is important to keep in mind the unique requirements of your project when deciding on the data splitting approach for machine learning..