Interview questions
Preparing for a data engineering interview at Capgemini? This page contains 27 real interview questions sourced from verified Capgemini interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
Capgemini data engineering interviews typically focus on Spark/Big Data, SQL, and Behavioral. The interview bar skews toward harder problems (11 hard vs. 9 easy), suggesting emphasis on depth and system-level thinking.
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 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.
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