Chapter 3 – Data Cleaning Fundamentals and Principles

1. B – Transforming data into a masterpiece – The aim of data cleaning in the data preparation process is to refine and enhance raw data, ensuring it is accurate, consistent, and high-quality for effective analysis
2. B – To prevent a cycle of perpetual data cleaning – While the other answers may have some truth to them, they do not describe why it is essential to establish a framework and principles for data cleaning efforts
3. B – Data assessment, data profiling, data validation, data cleaning strategies, data transformation, data quality assurance, and documentation– These processes together are involved in the data cleaning process
4. A – Patterns, distributions, and outliers – Data profiling aids in recognizing patterns, understanding distributions, and identifying outliers, providing crucial insights for effective data cleaning and quality improvement
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