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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
Describe Spark's execution model: driver, executors, tasks, stages, and the role of the DAG scheduler
Explain the difference between RDDs, DataFrames, and Datasets and when to use each
Explain Spark's lazy evaluation and how it enables query optimization via the Catalyst optimizer
Describe how Spark manages memory: storage vs execution memory and the role of serialization
Topics covered
Spark overviewCluster architectureSparkContextTransformations vs actionsLazy evaluationRDDsDataFrames vs RDDsRDDs vs DataFrames vs DatasetsDAG schedulerStages and shufflesCatalyst optimizerTungsten execution engineSpark memory modelSerialization
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“At a high level, what problem does Apache Spark solve, and how does it differ from the earlier MapReduce paradigm?
“Can you explain what Tungsten is and how it relates to Spark's performance at the execution layer?
“What is data locality in Spark, and how does it influence scheduling decisions?