More in Analytics & Metrics — page 11
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.
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.
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…
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'.
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.
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.
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.
Stakeholder claims correlation implies causation. How do you investigate?
This tests your scientific rigor beyond the simple "correlation isn't causation" mantra. Acknowledge the finding, probe for confounding variables, suggest cohort analysis, and propose an A/B test. A red flag is reciting the mantra without a concrete plan.
How would you design a product management dashboard?
This tests your ability to structure data hierarchically for a specific persona (PM). A great answer outlines a 3-tier structure: summary KPIs, trend analysis, and drill-downs. A red flag is listing charts without explaining how they guide PM decisions.
Bar Chart vs. Line Chart for Market Share Comparison?
This tests your grasp of visualization fundamentals for categorical vs. time-series data. A bar chart is correct for comparing discrete categories (companies) at one point in time. A line chart wrongly implies a trend. Red flag: justifying a line chart.
When is a pie chart an appropriate visualization?
Tests your grasp of data viz principles for part-to-whole data. A good answer defines this use case (e.g., market share), then lists pitfalls like too many slices or comparing multiple pies. A red flag is defending pie charts for complex data.
How do you manage schema evolution in an analytics pipeline?
This tests your grasp of data as a contract. A great answer covers schema registries, backward compatibility, and a phased rollout from producer to warehouse. A red flag is only mentioning an `ALTER TABLE` command, ignoring the entire data pipeline.
Challenges of Grouping by High-Cardinality Dimensions
This tests your grasp of system-level impacts of data shape. A good answer explains how high cardinality strains memory during aggregation, reduces compression, and inflates index size, leading to slow, expensive queries. A red flag is just saying 'it's slow'.
Trade-offs: Pre-aggregation vs. Querying Raw Data
Tests your grasp of data system trade-offs. A great answer weighs pre-aggregation (fast, cheap, stale) against querying raw data (slow, costly, fresh, flexible) and proposes a hybrid solution. A red flag is declaring one method universally superior.
How do you handle duplicate events in an analytics pipeline?
Tests your grasp of data integrity and idempotent processing. Explain how duplicates inflate COUNT(*), then propose adding a unique event_id and using a stateful stream processor to track seen IDs. Mention query-time COUNT(DISTINCT event_id) as an alternative.
Explain the star schema and its advantages for analytics
This tests your grasp of OLAP vs. OLTP data modeling. A great answer defines fact/dimension tables, explains how denormalization leads to fewer joins and faster queries, and contrasts this with 3NF's focus on write integrity.
How to diagnose a slow dashboard query?
This tests systematic debugging of a data problem. A good answer investigates the query plan first, then the table's physical layout (partitioning/clustering), and finally the BI tool and warehouse load. A red flag is jumping to a solution without diagnosis.
Why use median/p95 for API latency instead of the mean?
This tests if you understand how long-tail distributions make averages misleading for user experience. A good answer explains that median (p50) shows the typical user, while p95 captures the worst-case experience.
Design a Scalable Data Governance Framework
This tests your grasp of decentralized data architectures like Data Mesh. A great answer proposes a federated model with domain ownership, data as a product, and a self-serve platform.
How do you debug a data quality issue in a complex pipeline?
Tests systematic debugging in distributed systems. A great answer prioritizes containment, traces data lineage from report to source, and proposes specific observability tools.