Databricks Certified Data Engineer - Associate
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In a scenario where you are working with a large dataset in Azure Databricks that contains a 'product_description' column with text descriptions of products, you are tasked with extracting the product name from each description. The product name is always the first word in the description. Considering the need for efficiency and accuracy in processing large datasets, which of the following Spark SQL queries would you use to achieve this task? Choose the best option that correctly extracts the product name while ensuring optimal performance.
In a scenario where you are working with a large dataset in Azure Databricks that contains a 'product_description' column with text descriptions of products, you are tasked with extracting the product name from each description. The product name is always the first word in the description. Considering the need for efficiency and accuracy in processing large datasets, which of the following Spark SQL queries would you use to achieve this task? Choose the best option that correctly extracts the product name while ensuring optimal performance.
Explanation:
Option A is the correct answer because it efficiently uses the SPLIT function to divide the 'product_description' string by spaces and then selects the first element, which is the product name. This method is both accurate and optimized for performance with large datasets. Option B is incorrect as it attempts to filter descriptions containing the literal 'product_name', which does not align with the task's requirements. Option C incorrectly applies the SUBSTRING_INDEX function, which is not suitable for this specific extraction task. Option D fails to address the requirement by merely selecting the first row's description as the product name, which is not a solution for the entire dataset.