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Generate a monthly cohort retention table from user events
This tests your ability to translate a business metric into a multi-step SQL query. A great answer defines cohorts by first activity, maps subsequent activity to period indices, counts distinct users, and pivots the result.
Average latency is up, but p99 is flat. Why?
This tests your grasp of latency distributions. Hypothesize that a large group of typical requests slowed, pulling up the average but not crossing the p99 threshold. Segment by endpoint or customer to find the cohort.
Is 20% higher retention from Feature X causal or correlational?
This tests your ability to distinguish correlation from causation. A great answer questions the data, identifies confounding variables (e.g., power users), and proposes a randomized A/B test as the gold standard to prove causality.
DAU dropped 10%. What user segments do you investigate first?
Tests your systematic problem-solving. First, clarify the metric and timeline. Then, segment by platform, geography, and user tenure (new vs. returning). A red flag is jumping to external causes before ruling out internal issues like a bad deployment.
How do you visually represent statistical uncertainty in a chart?
This tests your ability to communicate statistical nuance beyond simple averages. A great answer discusses error bars (specifying CI vs. SD), then moves to richer visualizations like graded error bars or violin plots.
Visualize Millions of Time-Series Data Points
Tests your ability to handle large datasets by combining backend downsampling (like LTTB) with frontend multi-resolution fetching and canvas rendering. A red flag is suggesting naive sampling (every Nth point) or focusing only on frontend libraries.
Explain pre-attentive attributes in data visualization
Tests your grasp of visual psychology in data viz. Define pre-attentive attributes (instantly processed visuals), give examples (color, size, shape), and explain using one to highlight outliers in a dense plot.
Feature A correlates with retention. Should we invest more?
Tests your ability to move beyond clichés to propose concrete analysis. A great answer questions the correlation, suggests cohort analysis or A/B testing, and probes for confounding variables. Red flag: just saying 'correlation isn't causation' with no plan.
How would you design a product management dashboard?
Tests your ability to structure data into a decision-making narrative. A good answer moves from a high-level summary (DAU) to trends (retention) and then actionable details (feature adoption). A red flag is simply listing charts without a narrative connection.
Bar Chart vs. Line Chart for Market Share Comparison?
Tests basic chart selection: comparing static categories vs. showing trends. A bar chart is correct for comparing discrete companies at one point in time. A line chart wrongly implies a time-series relationship. Red flag: choosing a line or pie chart.
When is a pie chart an appropriate choice for visualization?
Tests data viz principles for part-to-whole data. A good answer defines this use case, gives a clear example (market share), and lists pitfalls like too many slices or similar values. A red flag is defending them for complex data or time-series analysis.
How do you manage an event schema change?
This tests your understanding of data contracts and backward compatibility. A great answer details a phased, backward-compatible rollout (add nullable field), starting with consumers (warehouse) then producers, and mentions using a schema registry for…
Challenges of Grouping by a High-Cardinality Dimension
This tests your grasp of how data shape impacts system resources. A great answer explains that high cardinality explodes memory usage for aggregation state, increases CPU load, and hurts storage compression.
Trade-offs: Pre-aggregation vs. querying raw event data
This tests your grasp of data engineering trade-offs for analytics dashboards. A great answer contrasts pre-aggregation (fast, cheap, stale) with on-the-fly queries (slow, costly, fresh) and mentions hybrid solutions.
How do you ensure accurate counts with duplicate analytics events?
Tests your grasp of data integrity under at-least-once delivery. Explain why COUNT(*) is inflated, then propose deduplication using a unique event ID. Mention trade-offs of stateful processing. A red flag is ignoring the cost or the need for a unique ID.
Explain a star schema and its advantages for analytics
Tests your grasp of data modeling trade-offs for OLAP vs. OLTP. A good answer defines fact/dimension tables, then explains how denormalization and fewer joins improve query speed for analytics. A red flag is confusing it with a snowflake schema.
How would you diagnose a slow dashboard query?
This tests your systematic approach to performance tuning. A great answer investigates query optimization, strategic indexing/partitioning, and the data model (like star schema).
Why use p50/p95 over mean for API response times?
Tests your grasp of statistical distributions for UX metrics. A good answer explains how outliers skew the mean, while percentiles (p50, p95) better represent typical and worst-case user experiences.
Design a Scalable Data Governance Framework
This tests your grasp of decentralized data governance (Data Mesh). A great answer outlines four principles: domain ownership, data as a product, a self-serve platform, and federated computational governance.
How would you debug a critical data quality issue in a pipeline?
Tests your systematic debugging of a distributed system under pressure. A great answer contains the impact, traces data lineage backward from the report to the source, and then proposes preventative tooling.