Spark & Big Data questions from Aarete data engineering interviews.
These spark & big data questions are sourced from Aarete data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (6 of 7 are tagged hard). Recurring themes are partition, optimization, and spark — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Freecharge, so the preparation transfers across companies. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 7 curated questions: 1 easy, and 6 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (6), optimization (5), spark (3), window (2), and bigquery (1). Focusing on these topics will give you the highest return on your preparation time.
Start with the easy questions to warm up and solidify fundamentals. Hard questions often appear in senior and staff-level rounds; attempt them after you're comfortable with the basics. For each question, try answering before revealing the solution. Use our AI Mock Interview to simulate real interview conditions and get instant feedback on your responses.
Explain the difference between batch and streaming data processing in Data Fusion.
Explain the concept of preemptible VMs in Dataproc and their cost implications.
How do you configure autoscaling for a Dataproc cluster?
How do you manage dependencies between tasks in a Cloud Composer DAG?
How would you debug a failing Spark job running on Dataproc?
How would you handle a large-scale data shuffle in a Dataflow pipeline?
What are the advantages of using Dataproc over a traditional Hadoop setup?
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