Data Driven Decision Making Questions

Using metrics and analytics to inform operational and strategic decisions. Topics include defining and interpreting operational measures such as throughput cycle time error rates resource utilization cost per unit quality measures and on time delivery, as well as growth and lifecycle metrics across acquisition activation retention and revenue. Emphasis is on building audience segmented dashboards and reports presenting insights to influence stakeholders diagnosing problems through variance analysis and performance analytics identifying bottlenecks measuring campaign effectiveness and guiding resource allocation and investment decisions. Also covers how metric expectations change with seniority and how to shape organizational metric strategy and scorecards to drive accountability.

EasyTechnical

0 practiced

Explain the difference between Service Level Indicator (SLI), Service Level Objective (SLO), and Service Level Agreement (SLA). For an ML inference service propose three concrete SLIs (e.g., p95 latency, inference error-rate, model-accuracy-on-labeled-sample), suggest realistic SLO targets, and outline corresponding alerting actions when SLOs are violated.

HardTechnical

0 practiced

Discuss advanced causal inference methods applicable to AI product decisions: synthetic controls for aggregate counterfactuals, causal forests for heterogenous treatment effects, double/debiased machine learning for unbiased parameter estimation with ML nuisance functions, and front-door/back-door adjustments. For each method, state assumptions, required data structure, and an example product use case where it is appropriate.

MediumTechnical

0 practiced

Case study: A model performs significantly worse in certain geographies. Outline an analysis plan to determine whether the problem is caused by feature differences, label quality, data collection bias, or model overfitting. List statistical tests, per-region SHAP or feature-importance analyses, and remediation steps with metrics to measure improvement.

MediumTechnical

0 practiced

You ran a targeted, model-driven ad campaign and observed higher clicks but no revenue lift. Describe a rigorous analysis to measure true campaign effectiveness: design of holdout groups, uplift estimation, LTV analysis, attribution windows, and guardrail metrics. Explain how selection bias and targeting feedback loops can distort conclusions.

HardTechnical

0 practiced

You must allocate a limited quarterly GPU budget across multiple competing AI projects to maximize long-term business value. Propose a quantitative allocation framework: estimate expected-value-per-GPU-hour, incorporate uncertainty/variance via Bayesian priors, create an exploration budget for high-uncertainty projects, and define decision thresholds for funding. Describe how to operationalize prioritization and monitor outcomes.

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