Databricks Certified Data Engineer - Associate
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You are working on a project that requires processing a large dataset stored in Azure Databricks. The dataset contains a primary key 'id' and a timestamp column 'event_time'. Your task is to create a new table that ensures data uniqueness based on the 'id' column and converts the 'event_time' to a timestamp format for accurate time-based analysis. Considering the requirements for data uniqueness, correct timestamp conversion, and optimal performance, which of the following Spark SQL queries would you choose? (Choose one option)
You are working on a project that requires processing a large dataset stored in Azure Databricks. The dataset contains a primary key 'id' and a timestamp column 'event_time'. Your task is to create a new table that ensures data uniqueness based on the 'id' column and converts the 'event_time' to a timestamp format for accurate time-based analysis. Considering the requirements for data uniqueness, correct timestamp conversion, and optimal performance, which of the following Spark SQL queries would you choose? (Choose one option)
Explanation:
Option C is the correct choice because it effectively uses the DISTINCT keyword to eliminate duplicate rows based on the 'id' column and correctly casts the 'event_time' to a timestamp format using the CAST function, ensuring data uniqueness and accurate time representation. Option A fails to cast the timestamp, Option B uses GROUP BY which is unnecessary for simply removing duplicates, and Option D incorrectly applies FROM_UNIXTIME, which is not universally applicable for all timestamp formats.