More in Analytics & Metrics — page 9
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.
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 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.
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.
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.
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.
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'.
Explain event schemas and the purpose of a schema registry
Tests your grasp of data contracts at scale. A good answer defines schemas as contracts, a registry as the enforcer of compatibility (e.g., backward/forward), and explains how this prevents 'poison pill' messages and brittle analytics.
Design a Data Model for a Feature Adoption Dashboard
This tests your ability to translate a business need into a scalable star schema. A great answer defines a central fact table (e.g., fct_feature_usage) and its dimensions (dim_users, dim_features, dim_date).
Describe the role of dbt in a modern analytics stack
Tests your grasp of modern ELT patterns. A good answer explains how dbt uses SQL for in-warehouse transformations, enabling software engineering practices like version control and testing. A red flag is confusing dbt with a full ETL tool or an orchestrator.
Describe tracking a user event from frontend to BI tool
Tests your grasp of the modern data stack. A great answer outlines the five stages: frontend emission, ingestion, loading into a warehouse, transformation, and BI visualization. A red flag is describing only the frontend code and ignoring the data pipeline.
Correlate API Slowness with User Engagement
This tests your ability to design a controlled experiment and join disparate data. A good answer outlines an A/B test with an artificial delay, logging with shared IDs, and statistical analysis. A red flag is proposing a purely observational study.
Design a Real-Time Anomaly Detection System for E-commerce Events
This tests your ability to design a real-time data pipeline and apply ML to a business problem. Outline a streaming architecture (e.g., Kinesis), processing, and storage.
How would you measure P95 latency by geographic region?
Tests your ability to translate a business need into a concrete observability implementation. A good answer involves instrumenting the API with a histogram metric, adding a region label via GeoIP, and querying with `histogram_quantile`.
How would you capture UTM parameters for attribution?
This tests your grasp of the data lifecycle from capture to persistence. A good answer covers client-side parsing, cookie storage, and linking anonymous data to a user record upon sign-up. A red flag is forgetting to persist the data server-side.
Describe the client-side event for an 'Add to Cart' button
Tests your knowledge of standard analytics schemas (like GA4) and designing payloads for business analysis. A great answer names the 'add_to_cart' event, details the 'items' array with product data, and mentions user/session context.
Describe two methods for generating prediction intervals
This tests your grasp of uncertainty quantification. A great answer contrasts an analytical method (assuming normal errors, using multipliers like 1.96 for 95%) with a simulation method (bootstrapping residuals).
How do you diagnose and fix a model's degrading performance?
Tests your MLOps process for handling model decay. Name it "concept drift," then outline a plan: diagnose by comparing data distributions, solve with a targeted retraining strategy, and implement proactive monitoring. A red flag is just saying "retrain it."
Which model for forecasting with seasonality and trend?
This tests your knowledge of classical time series models. A good answer names Holt-Winters, explaining its level, trend, and seasonal components. It also discusses choosing between additive and multiplicative methods. A red flag is jumping to complex models.
Train-Test Split vs. Time-Series Cross-Validation
This tests your grasp of data leakage in temporal data. A good answer explains why random splits create lookahead bias, then details how rolling-origin validation respects time. A red flag is just describing methods without explaining *why* one is necessary.