Real interview questions asked at Capgemini. Practice the most frequently asked questions and land your next role.
Capgemini data engineering interviews test your ability across multiple domains. These questions are sourced from real Capgemini 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 purpose of the Bronze, Silver, and Gold layers in a data pipeline?
Explain the projects you have worked on, focusing on challenges and solutions you implemented.
Explain your journey as a data engineer and the projects you have worked on.
How do you handle team coordination and deadlines in complex projects?
Tell me about yourself and your professional background.
Data Factory vs. Databricks: When to use which?
Provide an example of a critical decision you made in a project and its impact.
Discuss how you handled null values or unstructured data in your previous projects.
How does indexing improve query performance in SQL?
How would you deal with data skewness in a join operation?
How would you deal with data skewness in a large dataset?
Solve a problem using a window function in Spark or SQL.
map() vs mapPartitions(): Highlight the difference between map (row-level transformation) and mapPartitions (partition-level transformation).
repartition() vs coalesce(): Explain when to use repartition() (increases partitions) vs coalesce() (reduces partitions).
Adaptive Query Execution (AQE): Discuss how AQE optimizes query execution in Spark dynamically based on runtime stats.
Cache() vs Persist(): Explain the difference and use cases for caching and persisting data in Spark with memory levels.
Define what a User-Defined Function (UDF) is and how to register it in PySpark.
Describe the cluster configuration used in your project, including memory allocation, number of nodes, and executor/driver settings.
Discuss how you integrated Azure services into your Spark application.
Discuss the process of moving files in Databricks File System (DBFS).
Explain the architecture of Spark, including its components such as driver, executor, and cluster manager.
List all the technologies you have worked on in your project (e.g., Spark, Hadoop, Hive, Databricks).
Solve the dataset transformation using PySpark.
Solve the grade assignment problem using a UDF in PySpark.
What performance optimization techniques have you applied in Spark, Sqoop, or Databricks?
Which Spark version are you using in your project, and why did you choose it?
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