Intermediate everything in Analytics & Metrics, page 6
Design an A/B Testing Framework
This tests your ability to design a scalable system with statistical rigor for non-experts. A great answer outlines config management, deterministic user hashing, a data pipeline for metrics, and a results UI that simplifies stats.
How would you design a data quality monitoring system?
This tests your systematic approach to data reliability. A strong answer defines quality dimensions (freshness, volume, schema), proposes specific checks, and outlines an alerting strategy. A red flag is listing checks without tying them to business impact.
Propose a technical architecture for a centralized Metrics Layer.
This tests your grasp of data governance and semantic layers. A great answer outlines a system with a central definition store (e.g., YAML in Git), a query engine, and an API, ensuring all teams get consistent metric results.
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.
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.
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.
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.
Explain time series stationarity and how to achieve it
Tests your grasp of core time series assumptions. Define stationarity (constant mean/variance over time), explain why models need it for stable predictions, and name methods to test and achieve it. A red flag is just saying the data looks 'flat'.
Primary vs. Guardrail Metrics in Experiments
Tests your grasp of risk management in A/B testing. A great answer defines a primary metric as the goal and a guardrail as a 'do no harm' check. A feature ships only if the primary improves without hurting guardrails.
What is the novelty effect in experimentation?
This tests your grasp of temporary user behavior changes that can invalidate A/B tests. A strong answer defines the effect, explains how it inflates metrics, and suggests running tests longer or segmenting by user tenure. A red flag is ignoring mitigation.
Why is stopping an A/B test early problematic?
Tests understanding of the 'peeking problem' in A/B testing. A good answer defines peeking, explains how it inflates false positive rates, and contrasts it with waiting for a pre-determined sample size. A red flag is not explaining the statistical mechanism.
How do you determine sample size and duration for an A/B test?
This tests your grasp of statistical power and business trade-offs. A good answer defines the four inputs (baseline, MDE, significance, power) to calculate sample size, then uses traffic to find duration.
Explain Simpson's Paradox with a user engagement example
Tests if you see beyond aggregate data. Define the paradox, give a numerical example where a feature fails overall but wins in segments (e.g., new vs. returning users), and name the confounding variable. A vague definition without numbers is a red flag.
DAU dropped 10% overnight. Is this a significant change?
Tests your use of statistical hypothesis testing on business metrics. Outline the process: state a null hypothesis (no change), choose a Z-test, calculate the p-value, and compare to an alpha of 0.05. A red flag is guessing causes before proving significance.
Explain correlation vs. causation with a software example
This tests your critical thinking about data and ability to avoid logical fallacies. A good answer defines both terms, then gives a software example where a third, confounding variable (like traffic) is the true cause of two correlated metrics.
A/B test p-value is 0.08, PM wants to ship. What now?
Tests if you can translate statistical risk into business terms for a PM. A good answer defines the 8% false positive risk, weighs it against the cost of shipping, and suggests next steps like running the test longer instead of just saying no.
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