Problem Solving and Analytical Thinking Questions

Evaluates a candidate's systematic and logical approach to unfamiliar, ambiguous, or complex problems across technical, product, business, security, and operational contexts. Candidates should be able to clarify objectives and constraints, ask effective clarifying questions, decompose problems into smaller components, identify root causes, form and test hypotheses, and enumerate and compare multiple solution options. Interviewers look for clear reasoning about trade offs and edge cases, avoidance of premature conclusions, use of repeatable frameworks or methodologies, prioritization of investigations, design of safe experiments and measurement of outcomes, iteration based on feedback, validation of fixes, documentation of results, and conversion of lessons learned into process improvements. Responses should clearly communicate the thought process, justify choices, surface assumptions and failure modes, and demonstrate learning from prior problem solving experiences.

MediumTechnical

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Design a test plan to validate a fix for duplicate records that appeared in a downstream analytics table. Include unit tests, integration tests against synthetic data, canary runs in production, backfill validation strategy, acceptance criteria, and rollback conditions.

EasyTechnical

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You're on-call and notice a production data pipeline DAG has stopped mid-run and no new data has been processed for the last hour. Describe, step by step, how you would triage and resolve the issue. Include what logs, metrics, and checkpoints you'd inspect, what safe actions you'd take first, how you'd communicate with stakeholders, and what short-term mitigations and long-term fixes you'd propose to prevent recurrence.

HardTechnical

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You observe intermittent MapReduce job failures correlated with long GC pauses and HDFS file-close timeouts. Describe how you'd collect and analyze evidence (GC logs, JVM profiler/JFR, HDFS metrics), identify whether the root cause is memory pressure, GC configuration, or HDFS latency, and propose immediate mitigations and long-term fixes.

EasyTechnical

0 practiced

Explain the CAP theorem and its practical implications for designing distributed data processing components (metadata store, orchestrator, or a small transactional DB used by ETL). For a metadata service coordinating pipelines, which consistency/availability trade would you choose and why?

MediumTechnical

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Describe an observability strategy to detect silent data corruption (bit flips, encoding errors, truncation) that can pass through transformations and surface only in analytics. List checks, end-to-end tests, alerting, and explain how you'd validate a suspected corruption and localize its source.

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