Data Interpretation & Dashboard Literacy Questions
Practice interpreting data visualizations, trend lines, and metric dashboards. Develop ability to identify what's noteworthy (seasonality, anomalies, correlations) vs. normal variation. Think about causation vs. correlation. Practice explaining what a metric trend means in business terms and what actions it might suggest.
MediumTechnical
111 practiced
Write a SQL query (assume BigQuery SQL) to compute weekly retention: for each signup_week (ISO week), compute the percent of users who return in the subsequent week. Table: events(user_id STRING, event_name STRING, occurred_at TIMESTAMP, signup_at TIMESTAMP). Return columns: signup_week, retention_next_week_pct. Describe assumptions you make.
HardTechnical
59 practiced
A product metric shows a large variance across regions. How would you determine whether to localize the product experience, apply different growth strategies, or normalize metrics for comparability? Propose an analysis plan and decision criteria.
EasyTechnical
80 practiced
A product dashboard shows many custom metrics defined differently across teams (e.g., 'activation' has three definitions). As a PM, propose a practical process to standardize metric definitions across the company, including stakeholders, artifacts (e.g., data dictionary), and rollout steps.
MediumSystem Design
77 practiced
You're building a self-serve analytics template for PMs to run ad-hoc funnel analyses. Describe the data model, required events and properties, and safeguards (e.g., definitions, sampling warnings) you'd include to ensure correct use.
MediumTechnical
77 practiced
A funnel for checkout indicates a 60% drop between 'Add to Cart' and 'Begin Checkout' for mobile web. Suggest a prioritized list of hypotheses (technical, UX, pricing) to explain the drop and describe one data-driven test for the top hypothesis.
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