ITGSS Certified Technical Associate: Project Management Practice Exam

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What is the purpose of Data Preparation?

  1. To visualize data through graphs

  2. To transform data for AI model use

  3. To collect data from various sources

  4. To delete unwanted data files

The correct answer is: To transform data for AI model use

The purpose of data preparation is primarily to transform data for use in AI models. This process involves cleaning, organizing, and structuring data in a way that makes it suitable for analysis and modeling. In the context of machine learning and artificial intelligence, raw data is often untidy, uneven, or incomplete, which can hinder the effectiveness of the algorithms. Data preparation includes activities such as handling missing values, encoding categorical variables, normalizing numerical data, and ensuring consistency across datasets. By carefully preparing the data, practitioners can improve the performance of AI models significantly. Properly prepared data ensures that the model can learn from the data or predict outcomes accurately. While visualization is an important aspect of data analysis and can help in understanding trends or anomalies, its role is not focused on making data suitable for AI models. Collecting data from various sources is a part of the data gathering phase rather than preparation itself. Similarly, deleting unwanted data files can be part of the data cleaning process, but it constitutes just one element of the broader data preparation stage. Thus, transforming data specifically for model use encapsulates the main objective of data preparation.