Data Analysis and Requirements Translation Questions
Focuses on translating ambiguous business questions into concrete data analysis plans. Candidates should identify the data points required, define metrics and key performance indicators, state assumptions to validate, design the analysis steps and queries, and explain how analysis results map back to business decisions. This includes data quality considerations, required instrumentation, and how analytical findings influence product requirements or architectural choices.
MediumTechnical
0 practiced
A business wants to reduce false positives in a fraud detection system. Translate this objective into measurable metrics and an analysis plan: propose precision/recall targets, cost-sensitive loss formulation, how to compute business cost per FP/FN, and data required to estimate these costs. State key assumptions and how you would validate them.
HardTechnical
0 practiced
You must quantify model bias across demographic groups for a face recognition classifier. Propose a concrete analysis plan: describe data sampling (balanced vs population), fairness metrics to compute (e.g., false positive/negative rates by group, calibration), statistical tests for significance, and a mitigation plan if disparities are found. Include privacy and labeling considerations.
MediumTechnical
0 practiced
Your user behavior logs appear to be missing values not at random (MNAR). Explain how MNAR affects analysis and describe at least two strategies to handle MNAR when translating results into product decisions (e.g., sensitivity analysis, auxiliary data collection). Include how you would validate assumptions.
MediumTechnical
0 practiced
Design a set of dashboards and key metrics to monitor the health of an image-generation API: include usage, latency (p50/p90/p99), per-model quality signals, cost per call, abuse/abusive-content rates, and sampling strategy for human evaluation. Specify instrumentation events, sample sizes for human eval per day, and alerting rules for production incidents.
MediumTechnical
0 practiced
Given the schema events(user_id STRING, channel STRING, amount FLOAT, occurred_at TIMESTAMP), write SQL to compute revenue attribution using (a) last-click attribution and (b) an exponential time-decay attribution. Explain assumptions, edge cases (multiple channels same timestamp), and how to scale to millions of users.
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