The most frequently asked window questions in data engineering interviews.
Master window for your next data engineering interview. These questions cover core concepts, advanced patterns, and real-world scenarios that interviewers test. This set leans toward the medium-difficulty band most real interviews actually live in (32 of 60). Recurring themes are window, partition, and spark — these patterns appear most often in real interviews and reward the deepest preparation. These questions have been reported across 53 companies including Swiggy and Aarete. Average answer is around 1 minute of reading — plan roughly 1 hour to work through the full set thoughtfully.
This collection contains 60 curated questions: 0 easy, 32 medium, and 28 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are window (60), partition (47), spark (28), sql (22), join (16), and optimization (12). Focusing on these topics will give you the highest return on your preparation time.
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.
Write an SQL query to find the second-highest salary from an employee table.
Demonstrate the difference between DENSE_RANK() and RANK()
Discuss differences between ROW_NUMBER(), RANK(), and DENSE_RANK(), and provide examples from your projects.
Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.
Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()
How do you handle late-arriving data in Spark Structured Streaming?
Briefly explain the architecture of Kafka.
Retrieve the most recent sale_timestamp for each product (Latest Transaction).
Explain SQL Window Functions with examples.
Implement a query to find the top 5 customers by total sales amount.
SQL query to find the second highest salary from each department.
Write an SQL query to find duplicate emails in a users table.
Triggers in ADF, especially tumbling window triggers.
What is a window function? Explain with an example.
Write a query to find the top three highest-paid employees in each department using window functions.
Write complex SQL queries involving multiple joins, subqueries, and data aggregation logic.
Convert complex SQL (CTEs, window functions, subqueries) to production-grade PySpark. Discuss when to use spark.sql() vs. DataFrame API, and the implications for testability, partitioning, and execution predictability.
Explain the difference between batch and streaming data processing in Data Fusion.
How would you implement a sliding window aggregation in Spark Structured Streaming?
Implement a Spark job to find the top 10 most frequent words in a large text file.
What is the small-file problem in Spark, and how do you solve it?
Write the PySpark code to find the second highest salary in each department.
Describe a time you had to make a difficult decision with limited information.
Copy Large Files from On-Premises to Azure in ADF
On-Premises to Cloud Integration Runtime
Calculate a 7-day moving average of orders for each city in the Swiggy database.
Fetch the rows with the highest scores for each student in a year.
Find orders exceeding $1,000 in the last 30 days.
Given the data with id, name, and department, how would you calculate how many employees are in each department?
Shell commands for renaming a file?
Grouping and aggregation functions?
Multiprocessing in Python - explain with example
Optimize a function to calculate moving averages of user engagement.
Add Row Numbers using window function in PySpark
Add a new column with the average salary by department.
Explain the architectural trade-offs when optimizing a query on 100M+ rows: indexing vs. partitioning vs. materialized views. When does each approach become cost-prohibitive or operationally burdensome, and how do you quantify impact?
Explain the concept of window functions in SQL and provide an example
How would you handle data type changes for an existing column?
How would you identify duplicate records based on a composite key in SQL?
How would you retrieve the first and last order for each customer from a sales table?
Identify and remove duplicate records from a table, keeping the most recent record based on a timestamp column.
Teradata to Hadoop migration and handling data with SCD Type 2?
Test SQL skills using advanced window functions such as LAG, LEAD, and DENSE_RANK.
What are Slowly Changing Dimensions (SCD), and how would you implement them for tracking customer data changes?
Conceptualize and design a real-time streaming data pipeline end-to-end.
Explain Delta Time Travel and the purpose of the vacuum command.
Explain how you would implement real-time analytics using a streaming platform like Kafka or Kinesis.
How would you design a Kafka-based pipeline for processing streaming data in real-time?
How would you handle a large-scale data shuffle in a Dataflow pipeline?
Steps to mount storage in Databricks.
Describe a strategy for implementing a real-time content delivery monitoring system.
Design a data pipeline for real-time analytics of e-commerce transactions. Ensure to include data ingestion, processing, storage, and visualization components.
Design a data pipeline for streaming analytics.
Design an ETL pipeline to process real-time stock market data.
How do you handle pipeline failures or delays?
How do you handle production deployment?
How do you handle schema evolution in a system with multiple data sources and consumers?
How do you monitor and troubleshoot data pipeline failures in Data Fusion?
How do you optimize data ingestion?
How do you pass global variables between pipelines?
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