Interview questions
Preparing for a data engineering interview at FedEx Dataworks? This page contains 16 real interview questions sourced from verified FedEx Dataworks interview experiences. Questions are sorted by frequency — the ones asked most often appear first.
FedEx Dataworks data engineering interviews typically focus on Spark/Big Data, SQL, and Cloud/Tools. The interview bar skews toward harder problems (7 hard vs. 3 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.
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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