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Explain pre-attentive attributes and give three examples
This tests whether you know preattentive attributes are decoded in <200ms to guide attention freely. Name three such as color hue, size, and motion; then encode one variable in a dense scatter plot so targets pop out. Never call this decoration or color all.
Explain pre-attentive attributes in data visualization
Tests designing high-signal UIs. Define pre-attentive attributes as visual cues processed instantly (e.g., color, size, shape). Apply one to make key data 'pop' in a dense chart.
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.
How do you build a performant visualization for millions of time-series points?
Tests end-to-end data reduction: backend bucket downsampling like LTTB preserves visual shape, frontend uses level-of-detail rendering and viewport culling. Red flag: naive every-Nth sampling that drops peaks or sending raw millions to the browser.
Strategy for Visualizing Millions of Time-Series Points
Tests your strategy for balancing performance and visual fidelity with large datasets. Propose backend downsampling with an algorithm like LTTB to preserve peaks, then discuss multi-resolution data fetching on the frontend.
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.
How would you visually represent statistical uncertainty in a chart?
Awareness that plotted points are perceived as exact truths. Replace isolated bars with intervals showing point estimate uncertainty; add hypothetical outcome plots to make values tangible. Offering p-values or raw means without visualizing uncertainty range.
How would you visually represent statistical uncertainty in a chart?
This tests your ability to accurately communicate statistical significance. A great answer discusses error bars (with 95% CIs), then more advanced options like gradient or violin plots, and frames the choice by audience.
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.
What user segments do you check first after a 10% DAU drop?
Validate by time, platform, and geography; then slice by new vs returning, channel, and feature usage to isolate the bleeding cohort.
DAU dropped 10%. How do you investigate?
Tests structured problem diagnosis. First, verify the data isn't corrupt. Then, segment the drop by user type (new vs. returning), platform (iOS/Android/Web), and geography to isolate the 'what' before hypothesizing the 'why'.
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 would you determine if Feature X causally drives higher retention?
Tests causal inference intuition for product metrics. Great answers propose a randomized holdback or instrumental variable, control for user intent, and estimate a local average treatment effect.
Is Feature X Causal for 20% Higher Retention?
This tests your ability to separate correlation from causation. A great answer first identifies confounding variables (e.g., power users), then proposes an A/B test to isolate the feature's true effect, and finally suggests quasi-experiments if a test isn't…
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.
Average latency up 50ms but p99 flat: diagnose the discrepancy
Tests if you know mean reflects full distribution while p99 is a threshold. Strong answers hypothesize body shift like cache misses or traffic mix changes, and demand histograms and segmentation by endpoint. Red flag: blaming outliers, which would raise p99.
Average latency is up 50ms, but p99 is flat. How do you diagnose this?
Tests your grasp of latency metrics. A rising average with flat p99 means the *bulk* of requests (p50-p90) slowed, not the tail. Hypothesize a common bottleneck and segment data by endpoint/user to find it. Red flag: blaming new, slow outliers.
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.
Write SQL to generate a monthly cohort retention table from raw events
Tests window functions and date truncation for cohort analysis. A strong answer finds each user's first month, counts returning users per period, and divides by cohort size. Aggregating all users without isolating acquisition month hides new-user churn.
How to query a monthly cohort retention table in SQL?
Tests your ability to translate a core business metric into a multi-step SQL query. A good answer finds each user's acquisition month, joins that back to their activity, and pivots the data into a cohort grid. A red flag is calculating aggregate retention.