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.

EasyTechnical

0 practiced

Explain reservoir sampling for selecting k samples from a stream of unknown length. Provide clear pseudocode for the algorithm, analyze its time/space complexity, and discuss scenarios when reservoir sampling is preferable to periodic or time-based snapshots.

HardSystem Design

0 practiced

Design a scalable, low-latency data lineage system able to answer queries like 'which upstream files/queries contributed to this output row?' at petabyte scale. Describe ingestion of lineage metadata, storage model (fine-grained vs coarse), query patterns, retention, indexing, and how you'd optimize for common queries while controlling cost.

MediumTechnical

0 practiced

A daily ETL that usually completes in 30 minutes now takes 3 hours. Outline a structured approach to root-cause analysis: what metrics and logs you compare between runs, what targeted experiments you'd run (sampleed runs, warm/cold cache), and how you'd isolate whether the cause is data volume, skew, external dependency, or config changes.

MediumTechnical

0 practiced

Implement reservoir sampling in Python: write a function reservoir_sample(stream, k) that returns k uniformly random samples from a stream iterator of unknown length. Ensure single-pass behavior and O(k) memory. Include handling when the stream length is less than k and give a couple of unit test examples.

HardTechnical

0 practiced

Discuss consistency models (strong, eventual, causal) and how each affects ETL pipelines, analytic correctness, and downstream ML feature generation. Give concrete examples where eventual consistency can create subtle label-feature skew and describe mitigation strategies.

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