A dataset contains customer email addresses, and you

Solution

Explanation: Regular Expressions (Regex) are a powerful tool for pattern matching and validation tasks, making them ideal for checking email formats. Regex allows the definition of patterns to identify valid email addresses, ensuring that they follow a format such as username@domain.extension . For example, the pattern: ^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$ validates most standard email formats. Regex is versatile, efficient, and widely supported across programming languages, making it a preferred choice for data validation tasks involving string patterns. Option A: SQL queries are more suited for database operations like filtering and retrieving data but lack the flexibility of Regex for string pattern matching. Option C: Min-Max Normalization is a scaling technique and does not perform validation. Option D: PCA is a dimensionality reduction method and is unrelated to string validation tasks. Option E: Data Sharding involves dividing data into smaller chunks for storage and scalability, irrelevant to data validation.