OKR and Metric Definition Questions

Covers translating strategic objectives into measurable key results and operational metrics that drive the right behaviors. Topics include writing clear objectives and specific, measurable key results, distinguishing leading and lagging indicators, defining primary metrics versus guardrail metrics, selecting absolute versus relative targets, avoiding perverse incentives, and ensuring metric hygiene through reliable instrumentation and data quality checks. Also addresses setting appropriate targets and time horizons, monitoring cadence, dashboard design and alerting for metric deviations, and using metrics to inform prioritization and continuous improvement without encouraging gaming.

HardTechnical

0 practiced

Define Service Level Objectives (SLOs) and Service Level Agreements (SLAs) for the 'daily revenue' metric in BI: propose freshness SLAs (max latency), completeness tolerances, acceptable error budgets, monitoring queries to validate SLOs, automated alerting thresholds, and runbook steps for remediation including rollback and consumer notification.

MediumTechnical

0 practiced

Schema: events(event_id BIGINT, user_id BIGINT, event_name TEXT, event_time TIMESTAMP, order_id BIGINT). Funnel steps: 'visit' -> 'signup' -> 'trial_start' -> 'paid_conversion'. Write a Postgres SQL query to compute daily funnel counts and per-step conversion rates for the last 30 days, and identify days where overall funnel conversion dropped by more than 15% relative to a 7-day moving average.

EasyTechnical

0 practiced

Define a primary metric versus a guardrail metric. For the OKR 'Increase product engagement', propose one primary metric and two guardrail metrics. Explain how you would track trade-offs, alert on adverse guardrail movements, and avoid optimizing the primary metric at the expense of user experience.

EasyTechnical

0 practiced

Given table events: event_id BIGINT PRIMARY KEY, user_id BIGINT, event_type TEXT, event_time TIMESTAMP, properties JSONB. Write a Postgres SQL query to (1) detect duplicate event_id, (2) compute percent of events with null event_time per day for the last 30 days, and (3) list top 5 event_types with highest null rates. Explain assumptions about late-arriving events and backfills.

MediumSystem Design

0 practiced

Design an alerting strategy for the daily revenue metric: define statistical and business-based thresholds, alert types (info/warning/critical), noise reduction techniques (smoothing, cooldowns, correlated suppression), escalation paths, and an on-call/runbook approach for investigating alerts. Explain how to avoid unnecessary pager noise.

Unlock Full Question Bank

Get access to hundreds of OKR and Metric Definition interview questions and detailed answers.

Join thousands of developers preparing for their dream job.