Technical Communication and Decision Making Questions
Focuses on the ability to explain technical solutions, justify trade offs, and collaborate effectively across engineering and non engineering stakeholders. Topics include articulating design decisions and their impact on reliability performance and maintenance, walking through solutions step by step, explaining algorithmic complexity and trade offs, asking clarifying questions about requirements, writing clear comments documentation bug reports and tickets, conducting and communicating root cause analysis, participating constructively in code reviews, and negotiating quality versus delivery trade offs with product and operations partners. Interviewers evaluate clarity of expression, reasoning behind decisions, and the ability to make choices that balance short term needs and long term quality.
MediumTechnical
0 practiced
You have a backlog of technical debt items: flaky integration tests, missing schema-evolution docs, a slow daily aggregation job, and an inefficient S3 layout costing storage. Describe a prioritization framework you would use to sequence these tasks and justify which items you'd address first and why. Include how you'd present this prioritization to product and engineering managers.
HardTechnical
0 practiced
Draft a detailed plan to investigate intermittent data corruption discovered in ML training data. The plan should cover evidence collection (checksums, sample snapshots, timestamps), hypothesis prioritization, controlled tests to reproduce the corruption, safe rollback versus repair strategies, communication to data scientists about model retraining or rollback, and long-term preventive measures including monitoring and validation.
MediumTechnical
0 practiced
You will request operations allow a rolling deployment that temporarily duplicates processing (doubling compute) to validate a new deduplication algorithm. Draft a short technical proposal for operations that includes the validation plan, risk assessment, rollback strategy, cost estimate for the extra compute window, and metrics you will use to accept or abort the rollout.
MediumTechnical
0 practiced
Explain algorithmic complexity and system-level trade-offs between two deduplication approaches for a 10 TB dataset: (A) external sort + dedupe, and (B) hash-partition-then-dedupe in distributed Spark. Discuss CPU, network shuffle, memory, disk IO, parallelism, failure modes, and which approach you would recommend in a cloud Spark environment and why.
EasyTechnical
0 practiced
Explain the structure of a concise root cause analysis (RCA) document for a production data pipeline incident targeted at both engineers and product managers. Include the key headings you would use (e.g., executive summary, timeline, impact, root cause, contributing factors, mitigation, action items, owners, verification plan) and describe the kind of content (metrics, logs, assumptions) that belongs under each heading.
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