Problem Solving Under Constraints Questions

Assess how candidates identify, prioritize, and resolve problems when faced with limited time, limited resources, changing requirements, or unclear information. This includes execution discipline to maintain delivery and unblock teams, pragmatic adaptation of designs or plans to meet constraints, handling ambiguity by making reasonable assumptions and iterating, communicating trade offs and risks to stakeholders, and demonstrating creative but practical solutions that preserve core quality objectives. It also covers applied troubleshooting for realistic business problems such as calculating retention cohorts, reconciling datasets of differing granularity, or debugging data quality and pipeline issues, with emphasis on clearly explaining approach, assumptions, and recovery steps.

HardTechnical

0 practiced

You must merge two very large sorted event files on disk (>100GB each) into a single deduplicated output ordered by timestamp. Memory is limited (≈4GB). Describe an external-memory merge strategy and provide Python-style pseudocode for a streaming merge that resolves duplicates using a bounded LRU cache and supports a configurable fuzzy matching of user_id (small Levenshtein threshold). Discuss performance and correctness trade-offs.

HardTechnical

0 practiced

You have budget for only 5k labeled examples but need a classifier for millions of examples. Propose a practical pipeline combining weak supervision (labeling functions), data programming, and active learning under compute constraints. Describe how you'd estimate and correct label noise, calibrate weak labels into probabilistic labels, and validate model readiness for production with minimal labeling budget.

MediumTechnical

0 practiced

You have a DAG where an upstream partitioning job intermittently fails with 'PartitionNotFoundError' on weekends. Given this log excerpt:

2025-11-15 02:10 ERROR PartitionNotFoundError: partition ds=2025-11-14 missing
2025-11-15 02:11 INFO upstream job completed

Outline debugging steps, craft a SQL query to find missing partitions across the last 30 days, and propose a low-risk permanent fix that minimally disrupts downstream consumers.

MediumTechnical

0 practiced

You need a daily churn prediction but only have weekly-aggregated features (e.g., weekly_spend, weekly_active_days). Describe methods to adapt features and modeling techniques to predict churn at daily granularity, including how to create proxy labels, assumptions you would document, and quick validation checks to assess bias introduced by aggregation.

HardTechnical

0 practiced

A key executive KPI changed abruptly and teams provide conflicting root-cause theories; executives demand an immediate explanation while politics push for a favorable narrative. You must lead the investigation under time and political constraints. Describe a structured approach to root-cause analysis, prioritizing actions that reduce business risk quickly, and an honest yet pragmatic communication strategy to executives and affected teams.

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