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.
HardSystem Design
0 practiced
Design an enterprise metrics registry and governance platform for AI teams that supports centralized metric definitions (name, owner, SQL definition), lineage tracking, metric versioning, automated tests (sanity and coverage), access controls, and integration with CI/CD and dashboards. Describe components, metadata storage choices, APIs, onboarding flow, and a policy for metric deprecation and migration.
MediumTechnical
0 practiced
Design a process to measure and improve label quality for a multi-class image dataset used for training. Include stratified sampling for audits, inter-annotator agreement metrics (Cohen's kappa), rules for relabeling, cost estimates for annotation, and triggers for incremental relabeling based on model confidence drift.
EasyTechnical
0 practiced
Describe how to estimate cost per prediction for an online inference service. Given: GPU cost = $3/hour, one GPU can serve 300 requests/sec at average latency 50ms, and you expect 1,000,000 requests/day. Compute an approximate cost per prediction (assume full hours and 24/7 usage) and list practical optimizations to reduce cost (e.g., batching, quantization, model distillation).
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.
HardSystem Design
0 practiced
Design a multi-touch attribution system for purchases with a 90-day conversion window influenced by AI-driven recommendations. Requirements: cross-device identity stitching, deterministic matching where possible, probabilistic matching fallback, handling delayed conversions, support for configurable attribution rules and incremental analysis. Provide data model sketches, pipeline steps, key SQL queries, and scale considerations for data volume.
Unlock Full Question Bank
Get access to hundreds of Data Driven Decision Making interview questions and detailed answers.
