All bites
The whole library, newest first. Filter by what you are here for, or pick a topic if you already know.
4330 bites
Page 121
How do you optimize a data warehouse for billions of rows?
Tests physical design in columnar warehouses at scale. Strong answers cover partition and cluster pruning, materialized views or rollups to reduce joins, caching and search indexes for hot paths, and reserved slots or autoscaling.
Optimizing a Slow, Expensive Data Warehouse for BI Dashboards
Tests your grasp of data warehouse architecture beyond basic SQL. A great answer covers partitioning/clustering, materialized views for pre-aggregation, and cost controls. A red flag is suggesting only query rewrites or just 'adding more compute'.
How would you optimize a slow, expensive data warehouse?
Tests your diagnostic approach to performance issues. A good answer first analyzes query patterns, then applies partitioning by date, clustering by high-cardinality keys, and materialized views for aggregations.
Design a near real-time pipeline to monitor orders per minute
Tests stream architecture and batch trade-offs. Outline: Kafka or Kinesis ingestion, Flink with tumbling windows, Druid or Pinot storage, Grafana alerts. Contrast batch on latency, exactly-once semantics, and cost. Red flag: calling cron SQL real-time.
Design a Near Real-Time Analytics Pipeline
Tests your ability to design a low-latency data system and articulate trade-offs. A good answer covers ingestion (Kafka), processing (Flink), storage (Druid), and visualization (Grafana), contrasting the architecture's low latency with a batch setup.
Design a near real-time analytics pipeline for a critical metric
This tests your grasp of stream processing trade-offs (latency, cost, correctness). Outline a 4-stage pipeline (ingest, process, store, visualize) with specific tech choices, contrasting its low-latency, high-cost nature with batch.
Describe client-side events and properties to track Export to CSV usage
This tests telemetry design for async actions. A strong answer defines three custom events—click, success, failure—with properties like location, file_size, error_code, and user_id, fired at the right lifecycle moments.
Track an 'Export to CSV' button's usage
This tests modeling a feature's full lifecycle (intent, success, failure) in analytics. Propose one custom event like export_interaction with a status parameter ('success', 'failure') fired on completion, not just on click.
Track an 'Export to CSV' button's usage and outcomes
This tests your ability to design a robust event schema, not just track a click. A great answer uses one custom event name with a 'status' parameter ('initiated', 'success', 'failure'). A red flag is suggesting multiple event names for one action.
Explain cohort retention and write a pseudo-query for May signups
Tests cohort retention vs aggregate DAU and SQL self-joins for Week 1, 2, and 4 retention from May signups. Strong answers define cohorts by signup date, use datediff, and left-join activity. Red flag: using calendar week instead of relative signup date.
Explain cohort analysis and write a retention query
Tests your grasp of user behavior metrics. First, define a cohort (e.g., May signups). Then, explain why it's better than aggregate retention. Finally, outline a query grouping users by signup week and checking for activity. A red flag is a flawed query.
Explain cohort analysis for user retention and write a pseudo-query
Tests your ability to use precise metrics. A good answer defines a cohort, explains why it isolates variables better than aggregate data, outlines the calculation, and provides a clear pseudo-query.
Design a data model for feature adoption tracking
Tests dimensional modeling for high-volume events so PMs can query Feature A not B without complex SQL. A strong answer uses an event fact table plus a materialized user-feature summary. Red flag: a wide user table with boolean columns per feature.
Design a data model for tracking feature adoption
This tests your grasp of data warehouse star schemas for analytics. Outline a fact table for events and dimension tables for users and features, explaining how this structure enables fast, ad-hoc cohort analysis for a product manager.
Design a data warehouse model for tracking feature adoption
This tests your grasp of data warehousing star schemas for efficient behavioral analysis. A strong answer proposes a central events fact table linked to users, features, and time dimension tables.
Design a real-time mobile analytics pipeline
Tests decoupling high-volume ingestion from low-latency querying. Strong designs use an event broker, a stream processor for windowed aggregates, and an OLAP database for sub-second dashboards.
Design a Real-Time Analytics Pipeline for Mobile Events
This tests your grasp of low-latency streaming architectures. A good answer outlines ingestion (SDK to Kafka/Kinesis), real-time processing (Flink/Spark), and sinking to a fast OLAP database (Druid/ClickHouse). A red flag is proposing a batch-based ETL design.
Design a Real-Time Analytics Pipeline for Mobile Events
Tests your grasp of low-latency, high-throughput design. A strong answer outlines ingestion (Kafka), stream processing (Flink), and a real-time OLAP database (Druid/ClickHouse). A red flag is proposing a slow, batch-only architecture.
Design an A/B test separating novelty from true long-term impact
Tests distinguishing novelty from stable effects. Strong answer: staggered rollout with difference-in-differences comparing early and late adopters over weeks. Red flag: extending the A/B test without modeling time-interaction or control maturation.
Design an experiment to isolate long-term impact from novelty effect
Tests if you can design experiments for long-term impact, not just short-term lift. A good answer involves a long-running test, segmenting users by tenure, and modeling the effect over time to find its stable asymptote.