Problem Definition and Framing Questions

Covers the skills and practices used to clarify, diagnose, and scope ambiguous business or product problems into actionable problem statements before proposing solutions. Candidates should demonstrate structured and insightful clarifying questions to understand business context, current and desired states, target users and user needs, success metrics and desired outcomes, constraints such as budget, timeline, technical dependencies, and compliance, stakeholder perspectives, and existing performance baselines. Includes separating symptoms from root causes, surfacing and testing hypotheses, identifying data to collect and analyze, performing root cause analysis, breaking complex problems into prioritized subproblems, and defining acceptance criteria and next steps or experiments to reduce uncertainty. Encompasses discovery techniques and basic user research to surface user pain points and opportunities, requirements scoping including scope boundaries, risks and trade offs, and the ability to write a concise problem statement in your own words. At senior levels also assess strategic framing, avoiding premature solutions, aligning stakeholders, and presenting an executive narrative that links diagnosis to measurable outcomes and implementation trade offs; for junior candidates emphasize curiosity, systematic thinking, and the ability to prioritize information needs rather than jumping to implementation.

MediumTechnical

56 practiced

You're scoping an MVP to enable content creators to monetize. Define in-scope and out-of-scope features, list success criteria for an initial launch (3-6 months), and call out key risks and trade-offs to include in the PRD so engineering and design understand constraints.

EasyTechnical

70 practiced

You're in a 30-minute kickoff with a stakeholder who reports 'engagement is down'. What 8 clarifying questions would you ask to convert this vague statement into an actionable problem diagnosis? Include at least two questions about data sources and two about user segments. Explain why each question reduces uncertainty.

MediumTechnical

104 practiced

Design an instrumentation plan for measuring onboarding funnel completion. Provide recommended event names and essential properties, explain how to handle deduplication and late-arriving events, and describe simple checks to validate data quality. Use this funnel: Visit → Sign-up → Email-verification → First-task-completion.

Sample Answer

Situation: We need a clear, product-facing instrumentation plan so analytics, growth, and engineering measure onboarding reliably: Visit → Sign-up → Email-verification → First-task-completion.

Events & essential properties- page_visit - user_id (nullable), anonymous_id, session_id, event_id (UUID), event_ts, page, referrer, utm_campaign- signup - user_id, anonymous_id, event_id, event_ts, signup_method (email/google), source, campaign_id- email_verification - user_id, event_id, event_ts, verification_method, email_hash- first_task_completed - user_id, event_id, event_ts, task_id, task_type, task_duration

Why these fields: user_id links identity after sign-up; anonymous_id and session_id allow stitching pre-signup; event_id + event_ts support dedup and ordering.

Deduplication strategy- Enforce idempotency at ingestion using event_id: drop duplicates on event_id.- If event_id not provided, dedupe by composite key (user_id or anonymous_id, event_type, floor(event_ts to 1s)) with a tolerance window.- Store both event_ts (client) and ingested_ts (server) to detect replays.

Handling late-arriving & out-of-order events- Adopt a watermark policy (e.g., 48-hour lateness window) for near-real-time dashboards; mark data as provisional.- Batch pipelines: run daily re-compute for last N days (N=7) to apply corrections.- Implement backfill logic that upserts by event_id and re-derives funnel state (first occurrence per user per event).- Tag events with ingest_reason (live/retroactive) so queries can choose to include/exclude backfills.

Simple data-quality checks- Ingestion sanity: - Event volume by type vs expected baseline (alert on >30% drop or spike). - Missing required props rate (e.g., percent of signup events missing user_id/anonymous_id < 1%).- Consistency/funnel checks: - Monotonicity: count(signup) <= count(page_visit) within same cohort window; alert if violated. - Email verification without signup: ratio(email_verification without prior signup within 30 days) should be near zero.- Latency: - p50/p95 of (ingest_ts - event_ts) per event type; alert if p95 > threshold (e.g., 24h).- Uniqueness: - Duplicate event_id rate should be ~0; alert if >0.1%.- Sampling/identity stitching: - Fraction of users who convert from anonymous_id -> user_id during funnel.- Regression tests: weekly cohort recompute for last 7 days and compare to previous run; fail if >2% diff.

Operational notes for PM handoff- Provide above spec as tracking plan to engineering with example payloads and schema types.- Prioritize implementing event_id and both timestamps first.- Agree SLAs for re-compute windows and dashboard staleness with analytics/engineering.- Instrument automated alerts into monitoring (Datadog/Snowflake alerts) and schedule a post-launch audit to validate real user flows.

EasyTechnical

64 practiced

Write a one-sentence problem statement for this situation: "After launching a new onboarding flow, completion rate fell from 80% → 60% among users who sign up via email in the past 14 days." Your statement should specify target user, current vs desired state, timeframe, and how you'd measure the metric.

MediumTechnical

68 practiced

You observe DAU decreased by 8% in the last week. Given the simplified schema below:

users_daily_metrics(user_id INT, platform VARCHAR, country VARCHAR, date DATE, dau_flag BOOLEAN)

Describe the SQL analyses or pseudo-queries you would run in your first 48 hours to scope the problem (segmentation, cohort checks, retention changes, recent releases), and explain why each analysis is useful.

Sample Answer

First 48-hour plan: run focused SQL analyses to narrow where the 8% DAU drop happened (who, what, when) and whether it’s structural or transient.

1) Overall trend check (confirm and scope)

sql

SELECT date, COUNT(DISTINCT user_id) AS dau
FROM users_daily_metrics
WHERE date BETWEEN '2025-11-01' AND '2025-11-21'
GROUP BY date
ORDER BY date;

Why: verifies drop timing, magnitude, and whether it's a one-day blip or sustained.

2) Platform and country segmentation (find affected cohorts)

sql

SELECT date, platform, country, COUNT(DISTINCT user_id) AS dau
FROM users_daily_metrics
WHERE date BETWEEN '2025-11-14' AND '2025-11-21'
GROUP BY date, platform, country
ORDER BY date, platform, country;

Why: isolates where drop concentrated (e.g., iOS vs Android, specific country) to direct engineering/marketing.

3) Week-over-week percent change by segment

sql

WITH week AS (
  SELECT platform, country,
    SUM(CASE WHEN date BETWEEN '2025-11-07' AND '2025-11-13' THEN dau_flag::int ELSE 0 END) AS prev_week_dau,
    SUM(CASE WHEN date BETWEEN '2025-11-14' AND '2025-11-20' THEN dau_flag::int ELSE 0 END) AS this_week_dau
  FROM users_daily_metrics
  GROUP BY platform, country
)
SELECT platform, country,
  prev_week_dau, this_week_dau,
  ROUND(100.0*(this_week_dau - prev_week_dau)/NULLIF(prev_week_dau,0),2) AS pct_change
FROM week
ORDER BY pct_change;

Why: highlights segments with largest declines for prioritization.

4) New vs returning users (acquisition vs engagement)

sql

-- assuming first_seen table or derive first date per user
WITH first_seen AS (
  SELECT user_id, MIN(date) AS first_date FROM users_daily_metrics GROUP BY user_id
)
SELECT d.date,
  SUM(CASE WHEN f.first_date = d.date THEN 1 ELSE 0 END) AS new_users,
  SUM(CASE WHEN f.first_date < d.date THEN 1 ELSE 0 END) AS returning_users
FROM users_daily_metrics d
JOIN first_seen f ON d.user_id = f.user_id
WHERE d.date BETWEEN '2025-11-14' AND '2025-11-20'
GROUP BY d.date;

Why: tells whether drop is loss of new installs or churn among existing users.

5) Cohort retention shift (7-day retention for recent cohorts)

sql

-- cohort by signup date
WITH cohorts AS (
  SELECT user_id, MIN(date) AS signup_date FROM users_daily_metrics GROUP BY user_id
), activity AS (
  SELECT c.signup_date, a.date, COUNT(DISTINCT a.user_id) AS active
  FROM users_daily_metrics a
  JOIN cohorts c ON a.user_id = c.user_id
  WHERE a.date BETWEEN c.signup_date AND c.signup_date + INTERVAL '13 days'
  GROUP BY c.signup_date, a.date
)
SELECT signup_date,
  SUM(CASE WHEN date = signup_date + INTERVAL '7 days' THEN active ELSE 0 END) AS day7_active,
  -- optionally compute pct of cohort size
  FROM activity
GROUP BY signup_date
ORDER BY signup_date DESC;

Why: detects if recent cohorts are retaining worse — indicates onboarding/regression issues.

6) Recent releases & correlated datesQuery product/release tracking (pseudo)

sql

SELECT release_id, deploy_date, components, notes FROM releases
WHERE deploy_date BETWEEN '2025-11-01' AND '2025-11-21';

Why: correlate deploys/feature flags with drop; if aligned, prioritize rollback/hotfix.

7) Error/Crash signal join (if metrics available)

sql

SELECT date, platform, SUM(crash_count) AS crashes
FROM crash_metrics
WHERE date BETWEEN '2025-11-14' AND '2025-11-20'
GROUP BY date, platform;

Why: spikes suggest quality regressions causing churn.

8) Quick user-level check for large account loss

sql

SELECT date, COUNT(DISTINCT user_id) AS dau, COUNT(*) FILTER (WHERE is_premium) AS premium_dau
FROM users_daily_metrics
WHERE date BETWEEN '2025-11-14' AND '2025-11-20'
GROUP BY date;

Why: ensure not driven by a few high-value accounts dropping.

Actionable next steps after analyses:- If platform/country-specific → escalate to platform engineers/partner teams.- If retention drop for new cohorts → audit onboarding flows, AB tests, and recent UI changes.- If correlated with release/crash → rollback or patch and monitor.- If acquisition drop → check ad channels, tracking tags, and attribution pipeline.

These queries prioritize speed and targeted investigation to guide cross-functional triage in the first 48 hours.

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