Distributed Data Processing and Optimization Questions

Comprehensive knowledge of processing large datasets across a cluster and practical techniques for optimizing end to end data pipelines in frameworks such as Apache Spark. Candidates should understand distributed computation patterns such as MapReduce and embarrassingly parallel workloads, how work is partitioned across tasks and executors, and how partitioning strategies affect data locality and performance. They should explain how and when data shuffles occur, why shuffles are expensive, and how to minimize shuffle cost using narrow transformations, careful use of repartition and coalesce, broadcast joins for small lookup tables, and map side join approaches. Coverage should include join strategies and broadcast variables, avoiding wide transformations, caching versus persistence trade offs, handling data skew with salting and repartitioning, and selecting effective partition keys. Resource management and tuning topics include executor memory and overhead, cores per executor, degree of parallelism, number of partitions, task sizing, and trade offs between processing speed and resource usage. Fault tolerance and scaling topics include checkpointing, persistence for recovery, and strategies for horizontal scaling. Candidates should also demonstrate monitoring, debugging, and profiling skills using the framework user interface and logs to diagnose shuffles, stragglers, and skew, and to propose actionable tuning changes and coding patterns that scale in distributed environments.

MediumTechnical

0 practiced

Explain executor memory components in Spark: JVM heap, off-heap, storage memory, execution memory, and overhead. As an AI Engineer running MLlib and vector operations, how would you set executor memory and cores (executor-memory, executor-cores, spark.memory.fraction) to reduce OOMs during shuffle and tensor creation?

HardTechnical

0 practiced

In PySpark, implement salting for a skewed join. Given DataFrames 'big(user_id, val)' and 'small(user_id, info)', write a function that salts the small table by replicating rows with salt keys and salts the big table by appending a deterministic salt column, then performs the salted join. Show code, explain how to choose salt factor and seed for reproducibility, and describe performance trade-offs.

EasyTechnical

0 practiced

Compare columnar formats like Parquet and row-based formats like CSV/JSON in the context of large-scale AI data pipelines. Discuss I/O efficiency, predicate pushdown, schema evolution, vectorized reads, and how format choice affects memory, shuffle volume, and query planning.

HardTechnical

0 practiced

Explain how to integrate GPU-accelerated preprocessing (image normalization, augmentation) into a distributed Spark pipeline using RAPIDS/cuDF or similar ecosystems. Discuss data movement between CPU and GPU, impact on shuffle and memory, GPU-aware scheduling (device isolation), and when GPU preprocessing provides net benefit versus CPU.

HardSystem Design

0 practiced

Design a distributed preprocessing and feature extraction pipeline for model training where raw data resides across three geographic regions and cross-region egress costs are significant. Propose strategies (local pre-aggregation, federated feature computation, sharded model training, periodic synchronization) to minimize cross-region shuffles while enabling global model training and explain the trade-offs in bias, freshness, and communication overhead.

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