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.
A
The query will return all rows from both df1 and df2 where the 'id' values match, excluding any rows without a match. This option suggests an inner join, which is not the intended operation here.
B
The query will return all rows from df1, including those without a matching 'id' in df2, and only the matching rows from df2, with NULL values for non-matching rows in df2. This option correctly describes the left join operation and is optimized for performance by minimizing the data shuffled across the network.
C
The query will return only the rows from df1 that have a matching 'id' in df2, along with the corresponding rows from df2. This option incorrectly describes the left join as it excludes rows from df1 without a match in df2.
D
The query will return all rows from df2, including those without a matching 'id' in df1, and only the matching rows from df1, with NULL values for non-matching rows in df1. This option describes a right join, not a left join.
E
The query will return all rows from df1 and df2, combining them in a way that includes all possible combinations of rows, regardless of 'id' matches. This option describes a cross join, which is not the intended operation here.