Interview questions
Preparing for a data engineering interview at LTIMindtree? This page contains 7 real interview questions sourced from verified LTIMindtree interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
LTIMindtree data engineering interviews typically focus on Spark/Big Data, SQL, and Python/Coding. There's a solid mix of fundamental and advanced questions, making it accessible for candidates at multiple experience levels.
Use the difficulty filters above to focus your preparation. For each question, attempt your own answer first, then compare with our expert solution. You can also practice these questions in our AI Mock Interview Coach for real-time feedback.
What is the difference between SparkSession and SparkContext in Spark?
What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?
Write a Python function to check if a string is a palindrome.
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.
List Comprehension - example
CSV Without Column Names/Schema - how to read
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