Metrics Selection and Diagnostic Interpretation Questions
Addresses how to choose appropriate metrics and how to interpret and diagnose metric changes. Includes selecting primary and secondary metrics for experiments and initiatives, balancing leading indicators against lagging indicators, avoiding metric gaming, and handling conflicting signals when different metrics move in different directions. Also covers anomaly detection and root cause diagnosis: given a metric change, enumerate potential causes, propose investigative steps, identify supporting diagnostic metrics or logs, design quick experiments or data queries to validate hypotheses, and recommend remedial actions. Communication of nuanced or inconclusive results to non technical stakeholders is also emphasized.
HardSystem Design
0 practiced
Design an automated monitoring and anomaly-detection pipeline to cover 200 business metrics across regions and product lines. Describe the architecture (ingestion, storage, compute for detection, metadata, ownership, UI), approaches to detection (statistical thresholds, seasonality-aware methods, ML), how you would prioritize alerts, and how you would measure pipeline effectiveness (precision/recall of alerts). Consider scalability, onboarding new metrics, and false-positive reduction.
EasyTechnical
0 practiced
As a Business Intelligence Analyst for an e-commerce checkout optimization initiative, explain the difference between primary and secondary metrics. Provide 3 candidate primary metrics and 3 secondary (diagnostic/guardrail) metrics you would propose for an experiment to increase checkout conversions. For each metric, include a short definition, unit of measurement, desired direction of change, and justification for why it belongs to the primary or secondary category.
HardTechnical
0 practiced
Design an approach to attribute revenue to marketing channels for a business with offline conversions and long conversion windows. Describe deterministic and probabilistic methods (last-click, multi-touch, Shapley/proration), propose experiments (geo holdouts, incrementality tests), list diagnostic metrics to validate attribution (incremental lift, CPA, ROAS), and describe how you'd reconcile vendor data with internal billing.
HardSystem Design
0 practiced
Design an experiment and metric selection plan to test a new pricing model that minimizes cannibalization of existing plans. Include primary metric(s) (incremental revenue), guardrails (churn, conversion), sample stratification (by user LTV), required duration and sample size considerations, monitoring plan, rollback triggers, and a post-experiment analysis plan to evaluate long-term effects.
MediumTechnical
0 practiced
Describe a step-by-step approach to using segmentation and cohort analysis to isolate the root cause of a sudden metric drop. Include which segments to examine first (e.g., app version, acquisition channel, geography), the order of queries/visualizations, and how to control for seasonality and overlapping campaigns.
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