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
Describe privacy and compliance trade-offs when instrumenting events that may contain PII (emails, phone numbers, IPs). Explain approaches for minimization, hashing vs tokenization (and their reversibility), access controls, data retention policies, and how to support user deletion requests (GDPR/CCPA) without breaking analytics.
HardTechnical
0 practiced
Propose a canonical, repeatable process that maps high-level business goals to operational KPIs. Include who should participate (PM, data scientist, data engineer, legal), artifacts to produce (metric spec, instrumentation checklist, dashboards), decision gates (experiment vs immediate rollout), and guardrails to avoid metric manipulation or conflicting incentives.
MediumTechnical
0 practiced
After a release you detect discrepancies between product events and analytics metrics. Outline a testing and validation approach to verify whether developers implemented the instrumentation spec correctly: automated contract tests, telemetry sampling, raw vs derived count comparisons, end-to-end smoke tests, and sign-off criteria for green deployments.
HardTechnical
0 practiced
Analyze trade-offs between deriving sessions via heuristics (e.g., 30-minute inactivity) versus relying on server-issued session IDs for sessionization in analytics. Consider correctness, instrumentation complexity, multi-device behavior, bot traffic, attribution of session metrics (session length), and implications for historical comparability when switching methods.
EasyTechnical
0 practiced
Given an events table schema:| column | type ||-------------|-------------------------------|| user_id | bigint || event_name | varchar || event_time | timestamp with time zone || properties | jsonb |Write an ANSI SQL query (Postgres/BigQuery compatible) to compute Daily Active Users (DAU) for the last 14 days grouped by date. Explain how you handle timezones, null user_id, and deduplication of multiple events per user per day.
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