Real interview questions asked at FedEx Dataworks. Practice the most frequently asked questions and land your next role.
FedEx Dataworks data engineering interviews test your ability across multiple domains. These questions are sourced from real FedEx Dataworks interview experiences and sorted by frequency. Practice the ones that matter most. This set leans toward senior-level depth (7 of 16 are tagged hard). Recurring themes are partition, spark, and optimization — these patterns appear most often in real interviews and reward the deepest preparation. Many of these questions also surface at Nihilent and Freight Tiger, 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 16 curated questions: 3 easy, 6 medium, and 7 hard. The distribution skews toward harder problems, reflecting the depth expected in senior-level interviews.
The most frequently tested areas in this set are partition (13), spark (7), optimization (7), join (5), sql (3), and window (2). 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.
Explain the differences between Repartition and Coalesce. When would you use each?
Explain the differences between a Data Lake and a Data Warehouse.
Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.
Write a SQL query to find top 3 earners in each department.
Explain how Adaptive Query Execution changes the economics of Spark tuning. What problems does it solve at runtime, and when might you still need manual intervention (e.g., salting, broadcast hints)?
Explain wide vs. narrow transformations and how they drive shuffle cost, failure domains, and pipeline design. When would you intentionally add a wide transformation, and how do you minimize its impact?
Architecturally, how do Job–Stage–Task boundaries in Spark's execution model impact cluster sizing, shuffle cost, and when would you deliberately collapse or split stages?
What are the key components of the Spark execution model (Job, Stage, Task)?
What is the difference between repartition and coalesce in Spark?
How to copy all 1000 tables from source to target in ADF?
Write Python code to print even numbers from a list.
Check for duplicates in a table.
Create Spark Session, read CSV, join, and write as table. Provide example code.
How do you give permission to a notebook to other users in Databricks?
How does Autoscaling work in Databricks and what are its benefits?
Provide example code for Drop Duplicates in PySpark.
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