Real interview questions asked at LTIMindtree. Practice the most frequently asked questions and land your next role.
LTIMindtree data engineering interviews test your ability across multiple domains. These questions are sourced from real LTIMindtree interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward fundamentals — 14 easy, 5 medium, and 10 hard questions. 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 Citi and Coforge, 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 29 curated questions: 14 easy, 5 medium, and 10 hard. There's a strong foundation of fundamentals-focused questions — ideal for building confidence before tackling advanced topics.
The most frequently tested areas in this set are spark (17), optimization (8), partition (8), python (5), sql (5), and join (5). 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. Medium-difficulty questions form the bulk of real interviews — spend the most time here and practice explaining your reasoning out loud. 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?
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.
Command to Read JSON Data and Options
Daily Data Volume - quantify
Describe a project you worked on, focusing on the data pipeline and your role.
What is Multiline option in JSON?
Case Class and StructType Syntax
Closure Function - explain
Count of Alphabets in String
List Comprehension - example
CSV Without Column Names/Schema - how to read
Case statement in SQL - explain
Coalesce function in SQL - explain
Filter Rows Where Employee Salary > Manager Salary
Find 3rd Highest Salary
No Column Names in CSV - how to handle
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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