Spark & Big Data questions from LTIMindtree data engineering interviews.
These spark & big data questions are sourced from LTIMindtree data engineering interviews. Each includes an expert-level answer. This set leans toward senior-level depth (9 of 13 are tagged hard). Recurring themes are spark, optimization, and partition — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Altimetrik and American Express, 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 13 curated questions: 4 easy, and 9 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are spark (10), optimization (8), partition (7), python (3), sql (3), and etl (2). 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.
What is the difference between SparkSession and SparkContext in Spark?
When would you architecturally choose Dataset[T] over DataFrame in a Scala Spark pipeline, and what are the scalability and portability trade-offs? Include type-safety benefits vs. operational constraints.
Design a cost-aware resource strategy for a Databricks workload with spiky and batch jobs. Explain Dynamic Resource Allocation, when to disable it, and how min/max executors and spot instances affect cost and SLAs.
Accumulator and Broadcast Variables - explain
Describe building custom JARs for Spark jobs
Describe the projects emphasizing Spark, Hadoop, or Azure for large-scale data processing
Load CSV from HDFS
Memory Tuning in Spark
Performance Tuning Techniques for Spark
Production Experience - deploying and monitoring Spark jobs
Spark Session Command - how to create
Spark Submit - command syntax
Worked with UDFs - share examples
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