Given two strings (Word 1 and Word 2) and a list of valid words from a dictionary, return the minimum number of operations required to convert Word 1 to Word 2.

This problem is a classic example of calculating the Levenshtein distance, which measures the minimum number of single-character edits (insertions, deletions, or substitutions) required to change one word into the other. The standard approach uses dynamic programming to efficiently compute this distance. A 2D array (or table) is typically used, where each cell dp[i][j] stores the Levenshtein distance between the first i characters of Word 1 and the first j characters of Word 2. The table is filled iteratively, with the value of each cell depending on the values of its neighbors and whether the characters at the current positions match. The final answer is found in the bottom-right cell of the table, representing the distance between the full strings.