A join between a 2 TB fact table and what the engineer believed was a small dimension table causes repeated executor `OutOfMemoryError` failures: ```python fact = spark.read.format("delta").load("/mnt/gold/fact_sales") dim = spark.read.format("delta").load("/mnt/gold/dim_store") result = fact.join(broadcast(dim), "store_id") ``` Investigation shows `dim_store` has grown to 12 GB over time and `spark.sql.autoBroadcastJoinThreshold` is set to `-1` in the cluster's Spark config. Which two actions would most directly resolve the OutOfMemoryError? (Choose 2 answers.)
Select one answer.
Purchase Full Access to view answer options & explanation
The first 118 questions in this bank are completely free. Unlock Full Access on the exam page to view all answer choices, verified answers, explanations, and community discussions for all 392 questions.
