Back to Data Engineering
About this interview
A technical interview on Batch Processing, pitched at the medium level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Identify the causes of data skew and shuffles and common mitigation techniques (salting, repartitioning)
Explain broadcast joins and when they eliminate shuffle entirely
Describe partitioning strategies for writing to object storage and how they affect read performance
Reason about Spark tuning levers: parallelism, executor sizing, and adaptive query execution
Topics covered
ShufflesParallelismPartitioningBroadcast JoinsData SkewObject Storage PartitioningAdaptive Query ExecutionJoin StrategiesQuery OptimizationCachingExecutor SizingFault Tolerance and StragglersDiagnosis
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“What is a shuffle in Spark, and why does it tend to be one of the most expensive operations you can trigger?
“How does AQE's dynamic coalescing of shuffle partitions help compared to statically setting spark.sql.shuffle.partitions?
“Explain the concept of a skewed join where one side is large and one side is large but skewed. When would you use broadcast join versus salting versus AQE skew handling to fix it?