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Databricks Databricks-Certified-Associate-Developer-for-Apache-Spark-3.5 Question Answer

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A data engineer has written the following code to join two DataFrames df1 and df2:

df1 = spark.read.csv("sales_data.csv")

df2 = spark.read.csv("product_data.csv")

df_joined = df1.join(df2, df1.product_id == df2.product_id)

The DataFrame df1 contains ~10 GB of sales data, and df2 contains ~8 MB of product data.

Which join strategy will Spark use?

Shuffle join, as the size difference between df1 and df2 is too large for a broadcast join to work efficiently.

Shuffle join, because AQE is not enabled, and Spark uses a static query plan.

Shuffle join because no broadcast hints were provided.

Broadcast join, as df2 is smaller than the default broadcast threshold.

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