Real interview questions asked at Coforge. Practice the most frequently asked questions and land your next role.
Coforge data engineering interviews test your ability across multiple domains. These questions are sourced from real Coforge interview experiences and sorted by frequency. Practice the ones that matter most.
What are traits in Scala, and how are they different from classes?
What is the difference between cache() and persist() in Spark? When would you use each?
What is the difference between groupByKey and reduceByKey in Spark?
What is the difference between narrow and wide transformations in Apache Spark? Explain with examples.
What is the difference between partitioning and bucketing in Spark, and when would you use bucketing?
Can you explain the architecture of Apache Spark and its components?
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.
Can you explain your experience with Jenkins in your project?
Explain your project and the technologies used so far.
How do you check the memory of your laptop using Linux commands?
How are strings handled in Scala? How are they different from Java strings?
Write a Scala code to print prime numbers.
Given the data below, explain the results of different types of joins: Inner Join, Left Join, Right Join. Will a schema be created?
Can you explain dynamic resource allocation in Spark? How does it help optimize job performance?
Explain the DAG in Spark and how it plays a role in execution.
Have you worked with UDFs in Spark? When do you use them, and how do they differ from built-in functions?
How do you handle schema evolution in Spark, especially when reading data from sources like Parquet or Avro?
How do you handle very large datasets in Spark to ensure scalability and efficiency?
How many stages are created in a Spark job, and how are they formed?
How would you handle unstructured data in Hive?
What are the key performance tuning techniques you apply in Spark jobs to improve performance?
What is data shuffling in Spark, and how do you minimize its impact on job performance?
What is one disadvantage of using Scala for data engineering tasks?
What is the command to import data from HDFS to Hive?
What is the difference between map and flatMap in Spark transformations?
What is the difference between partitions and repartitions in Spark, and when do you use each?
Explain how Spark handles fault tolerance. How does it recover from node failures?
How do you ensure data quality in a big data pipeline, and what strategies do you use for data validation?
How does Spark handle distributed computing, and what challenges have you faced while working on distributed systems?
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