Nvidia
Hardware and AI software leader powering the global generative AI revolution.
4 Rounds
~25 Days
Very Hard
The Interview Loop
Recruiter Screen (30 min)
Standard fit check, behavioral questions, and resume overview.
Technical Loop (3-4 Rounds)
Deep dive into domain knowledge, coding, and system design.
Interview Question Bank
Data Engineer
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Technical
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hard
In Apache Spark, you are joining a massive telemetry fact table with a smaller dimension table, but the job keeps failing with an OutOfMemory (OOM) error due to data skew. How do you troubleshoot and resolve this?
#Apache Spark
#Performance Tuning
#Data Skew
Data Engineer
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Technical
•
hard
Explain how you would achieve exactly-once processing semantics in a Kafka to Spark Streaming pipeline. What are the trade-offs?
#Apache Kafka
#Spark Streaming
#Distributed Systems
Data Engineer
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Technical
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medium
What is the difference between narrow and wide transformations in Spark? Provide examples of each and explain how they impact the Catalyst Optimizer's physical execution plan.
#Apache Spark
#Distributed Computing
#Catalyst Optimizer
Data Engineer
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Technical
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hard
Explain how Apache Spark handles data skewness. How would you resolve a severely skewed join in a pipeline processing terabytes of autonomous driving sensor data?
#Apache Spark
#Performance Tuning
#Distributed Computing
Data Engineer
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Technical
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easy
What is the difference between `repartition()` and `coalesce()` in Apache Spark? When would you use one over the other?
#Apache Spark
#Data Shuffling
#Resource Management
Data Engineer
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Technical
•
medium
Explain the mechanics of Spark's Catalyst Optimizer. How does it transform a logical plan into a physical plan?
#Apache Spark
#Query Optimization
Difficulty Radar
Based on recent AI-sourced data.
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Senior EngineerFocuses on core competencies, system constraints, and clear communication.
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