Structured Problem Solving and Decomposition Questions
Frameworks and practices for framing ambiguous problems, decomposing complexity into tractable components, and designing an investigative plan. Includes problem framing, hypothesis tree and funnel approaches, logical decomposition of metrics and processes, prioritization of diagnostic paths, and communicating a clear problem statement and scope. Emphasis on translating vague business issues into testable questions, mapping metrics to subcomponents, and sequencing investigations based on impact and likelihood.
MediumTechnical
0 practiced
Given this table of candidate root causes, create a prioritization ranking using impact (high/medium/low), likelihood, and effort. Provide numeric scoring, normalize if needed, and pick the top 3 investigative actions with justification.| cause | estimated-impact | data-availability | effort ||---|---:|---|---:|| campaign-mis-tagging | high | high | low || frontend-bug | high | medium | high || traffic-quality | medium | low | medium || price-change | low | high | low |
EasyTechnical
0 practiced
In your own words, define 'structured problem solving' as applied to data science projects. Explain why it matters when addressing ambiguous business issues, and list three measurable outcomes (for example: time-to-insight, false-positive reduction, or reproducibility) that indicate your approach improved the analytic workflow. Give a short example of a problem that benefits from this approach.
MediumTechnical
0 practiced
After merging two ETL pipelines you observe discrepancies in user counts between the old and new systems. Design a prioritized diagnostic plan to identify root causes: specify the list of data checks (aggregations by date, partition, source), sampling strategies to validate records, log comparisons, and short-term mitigations to reduce business impact while debugging.
HardTechnical
0 practiced
Implement a Python function that approximates per-feature Shapley contributions to a scalar metric change using Monte Carlo sampling over random feature permutations. Inputs: baseline_features (dict), current_features (dict), metric_fn(mapping)->float, n_samples (int). Describe algorithmic complexity, caching strategies to reduce repeated evaluations, and how you'd handle dependent/correlated features.
MediumTechnical
0 practiced
You have table events(user_id, channel, revenue, event_date). Write an SQL query that computes per-channel revenue for two time periods (T0 and T1), the absolute and percent change per channel, and the percentage contribution of each channel to the overall revenue change (i.e., per-channel_delta / total_delta). Assume T0 and T1 boundaries are given as dates. Use CTEs and handle zero/NULL totals gracefully.
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