Easy everything in Analytics & Metrics, page 4
Mean vs. Median for API Response Times?
Tests if you know latency data has outliers. A good answer chooses median because it's robust to extremes. Explain that mean gets skewed by a few slow requests (e.g., GC pauses), hiding the typical user experience.
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.
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.
Bar Chart vs. Line Chart for Market Share Comparison?
Tests basic chart selection: comparing static categories vs. showing trends. A bar chart is correct for comparing discrete companies at one point in time. A line chart wrongly implies a time-series relationship. Red flag: choosing a line or pie chart.
When is a pie chart an appropriate choice for visualization?
Tests data viz principles for part-to-whole data. A good answer defines this use case, gives a clear example (market share), and lists pitfalls like too many slices or similar values. A red flag is defending them for complex data or time-series analysis.
Why use p50/p95 over mean for API response times?
Tests your grasp of statistical distributions for UX metrics. A good answer explains how outliers skew the mean, while percentiles (p50, p95) better represent typical and worst-case user experiences.
What is a data schema and why enforce it on ingestion?
This tests your grasp of data governance. Define a schema as a data blueprint. Explain that enforcement on ingestion prevents "garbage in, garbage out" by validating types and formats, ensuring data is usable for analytics.
How would you validate user-submitted email addresses at ingestion?
Tests your understanding of data validation beyond simple regex, focusing on robustness and system-level thinking. A good answer covers format checks, DNS/MX record validation, and blocking disposable services.
How would you handle 10% nulls in a key column?
This tests your ability to diagnose data issues before solving. First, investigate why data is null. Then, discuss trade-offs of dropping vs. imputing with the mean or median, considering the impact on the dashboard's accuracy.
What is a data warehouse vs. a transactional database?
Tests your grasp of read-optimized (OLAP) vs. write-optimized (OLTP) systems. A great answer defines warehouses for analysis, contrasts them with transactional DBs for operations, and explains the resulting differences in workload, schema, and data structure.
Build a pipeline to load CSVs into a database
Tests your grasp of event-driven architecture and basic ETL. A good answer outlines a trigger (storage event), a processing function (serverless), and a destination (database), mentioning error handling. A red flag is describing a manual or cron-based process.
ETL vs. ELT: Key differences and when to use each?
This tests your understanding of modern data architecture trade-offs. A good answer explains the T vs. L order, then links ETL to structured, legacy systems and ELT to flexible, cloud-native data lakes/warehouses.
Translate 'increase engagement' into a technical measurement plan
This tests your ability to translate vague business goals into concrete metrics. First, clarify the goal with the PM. Then, propose specific, measurable proxy metrics (e.g., DAU/MAU, session length). Finally, outline the instrumentation plan.
Randomized Controlled Trials: The Gold Standard for 'Did It Work?'
An RCT is a science fair experiment for business decisions, isolating one change to see its true effect. It's used in A/B tests to prove a new feature worked. The biggest footgun is peeking at results early, which can lead to false conclusions.
ETL: The Assembly Line for Your Data
ETL (Extract, Transform, Load) is an assembly line for data, moving it from various sources into a single destination for analysis. It's used to populate data warehouses by combining data from databases, logs, and APIs into a unified view.
Single Source of Truth (SSOT): One Place for Every Fact
A Single Source of Truth (SSOT) means every piece of data has one authoritative home. Instead of emailing report_v3.docx, you share one link. This prevents conflicts between billing and shipping data.
HiPPO: Highest Paid Person's Opinion
HiPPO stands for "Highest Paid Person's Opinion," where a senior leader's gut feeling overrules data. This occurs when deciding on features without A/B tests. The footgun: a HiPPO's intuition can kill great ideas or greenlight costly failures.
Data Literacy: Reading the Story in the Numbers
Data literacy is reading comprehension for numbers. It's the ability to turn raw data into a coherent story, a crucial skill for anyone using dashboards or A/B test results. The biggest footgun is confusing correlation with causation.
Training vs. Test Sets: Judging a Forecast's True Accuracy
Don't judge a forecast by how well it fits the data it was trained on. To measure true accuracy, split your data into a training set for model fitting and a test set for evaluation, using data the model has never seen before.
Tag Management Systems: Control Your Analytics Snippets
A Tag Management System (TMS) is a single container for all the analytics and marketing scripts you'd otherwise hardcode. Instead of asking engineers to add new scripts, you add them via a web UI.
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