Technical interview guide

Probability Distributions

The handful of named distributions (normal, binomial, Poisson) that show up repeatedly, and what each models.

Read: 44 min
Practice MCQs: 25
Interview QA: 25
Edition: v6
Editorial status: Reviewed
Relevant for: AI EngineerBusiness Intelligence DeveloperComputational BiologistData AnalystData ScientistMachine Learning EngineerProduct ManagerQuantum Software Engineer

Scope: Standards and library references current as of 2026-09: NIST/SEMATECH e-Handbook, SciPy, NumPy, statsmodels, and scikit-learn.

Interview QA

Treat each question like a live interview question: answer out loud first (structure, assumptions, tradeoffs), then open the model answer to spot gaps and rehearse a tighter follow-up.

Curated: 2026-10-05 · Written: 2026-10-05 · Reviewed: 2026-10-03

QA-1

Explain distributions as models of a generating process and how it changes a production decision.

QA-2

How would you reason about discrete versus continuous in a system you own?

QA-3

Walk through the Bernoulli and binomial distributions, including where engineers most often get it wrong.

QA-4

What does conversion rates and their variance guarantee, and what does it deliberately leave open?

QA-5

Describe the Poisson distribution and the evidence you would collect before relying on it.

QA-6

A teammate proposes a design that hinges on the exponential distribution and memorylessness. How do you evaluate it?

QA-7

Where does the normal distribution and where it comes from matter, and where is it irrelevant?

QA-8

Teach the log-normal distribution to an engineer who has only seen it as a rule of thumb.

QA-9

How do you distinguish between heavy-tailed power law distributions and thin-tailed distributions in real-world data, and what are the implications for estimation and risk?

QA-10

How would you test a claim that the system depends on the uniform distribution and its uses?

QA-11

How does the Negative Binomial relate to the Geometric distribution, and when do you choose Negative Binomial over Poisson to model counts?

QA-12

How does parameterisation differences between libraries change if the workload grows by two orders of magnitude?

QA-13

How do you interpret deviations from the reference line in a normal Q-Q plot to diagnose heavy tails, skewness, or bimodality?

QA-14

When would you refuse a design because of quantile plots for assessing fit?

QA-15

How would you explain goodness-of-fit tests at scale without using the usual slogan?

QA-16

What failure would you inject to check a team's understanding of mixture distributions?

QA-17

How should on-call treat an alert that names the chi-squared and t distributions as the cause?

QA-18

What belongs in a runbook section on the beta distribution, and what does not?

QA-19

How would you review a pull request whose risk is the empirical distribution?

QA-20

What trade-off does sampling from a distribution force that a junior answer usually skips?

QA-21

How would you brief product on why truncation and censoring delays a ship date?

QA-22

What is the smallest experiment that would change your mind about distributions in capacity planning?

QA-23

How does convolution and aggregate latency interact with rollback, and where do people ignore that?

QA-24

What would you ask a candidate who recites choosing a summary statistic but cannot apply it?

QA-25