Databricks Certified Data Engineer - Associate
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In a scenario where you are working with two DataFrames, df1 and df2, in a Spark SQL environment, df1 has the schema (id: int, name: string) and df2 has the schema (id: int, age: int). You are tasked with performing a left join operation between these two DataFrames to analyze customer data. The analysis requires that all customer names from df1 are included in the result, regardless of whether there is a matching age in df2. However, for those customers that do have a matching age, the age should also be included in the result. Considering the need to minimize computational resources and ensure the query's efficiency, especially with a significantly large dataset, which of the following statements not only accurately describes the result of a left join query between df1 and df2 but also suggests the most efficient execution plan? Choose the best option.
In a scenario where you are working with two DataFrames, df1 and df2, in a Spark SQL environment, df1 has the schema (id: int, name: string) and df2 has the schema (id: int, age: int). You are tasked with performing a left join operation between these two DataFrames to analyze customer data. The analysis requires that all customer names from df1 are included in the result, regardless of whether there is a matching age in df2. However, for those customers that do have a matching age, the age should also be included in the result. Considering the need to minimize computational resources and ensure the query's efficiency, especially with a significantly large dataset, which of the following statements not only accurately describes the result of a left join query between df1 and df2 but also suggests the most efficient execution plan? Choose the best option.
Explanation:
A left join operation in Spark SQL returns all rows from the left DataFrame (df1) and the matching rows from the right DataFrame (df2). If there is no match, the result will have NULL values for the columns from the right DataFrame. This ensures that all customer names from df1 are included in the result, with their corresponding ages from df2 if available. Option B correctly describes this behavior and also suggests the most efficient execution plan by minimizing the data shuffled across the network, which is crucial for large datasets. Option A describes an inner join, not a left join. Option C incorrectly describes the left join by excluding non-matching rows from df1. Option D describes a right join, and Option E describes a cross join, neither of which is the intended operation here.