Problem Decomposition Questions
Break complex problems into smaller, manageable subproblems and solution components. Demonstrate how to identify the root problem, extract core patterns, choose appropriate approaches for each subproblem, sequence work, and integrate partial solutions into a coherent whole. For technical roles this includes recognizing algorithmic patterns, scaling considerations, edge cases, and trade offs. For non technical transformation work it includes logical framing, hypothesis driven decomposition, and measurable success criteria for each subcomponent.
EasyTechnical
0 practiced
Explain what problem decomposition means for a data engineer and why it matters. Provide a concrete example: decompose the task "ingest daily CSV files from partner SFTP into a raw data lake and make them queryable by analysts" into at least five smaller components. For each component, state inputs, outputs, success criteria, estimated runtime, and potential failure modes.
MediumTechnical
0 practiced
Write a Python function that partitions a list of records (each with a key and size) into N tasks for parallel processing to balance load when keys are heavily skewed. Provide the algorithm and a working implementation that respects memory constraints and aims for near-equal total record counts per task.
HardTechnical
0 practiced
Given a Spark job that performs many chained transformations (map/filter/flatMap/groupByKey/reduceByKey) causing multiple shuffles and long runtime, explain how to decompose operations into stages, identify shuffle boundaries, and reorganize transformations to minimize shuffles and I/O. Provide concrete refactor techniques and metrics to measure improvement.
HardSystem Design
0 practiced
Compose a decomposition approach to migrate on-prem Hadoop workloads to cloud-managed services (EMR/Dataproc/Glue). Include inventorying jobs and dependencies, classifying workloads (ETL, ad-hoc, ML), estimating data transfer and network constraints, selecting managed alternatives, planning rewrites for service differences, validating parity, and rollback strategy. Cover security, IAM, and cost controls.
HardSystem Design
0 practiced
Design a comprehensive testing decomposition for data pipelines: unit tests for transforms, integration tests for connectors, contract tests for producers/consumers, schema tests, end-to-end staging, and CI gating. Provide examples of test inputs, expected outputs, and a strategy for non-deterministic streaming transforms.
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