Experimentation
experimentation
Practice Problems
Experiment Outcome Summary
Validate one outcome and cost record per randomized learner, then report each A/B arm's success rate, mean cost, and cost per success, plus absolute and relative lift, with null wherever a denominator is 0.
Behavioral Interviews
Behavioral for ML / Data Engineers
ML and data engineering loops grade for a cluster of behavioral signals that other engineering loops weight less heavily: experimentation rigor, the craft of being wrong with data and catching it yourself, data ethics judgement under tradeoff, ambiguity tolerance on problems where the right answer is not knowable in advance, and substantive collaboration with research and platform teams. The behavioral signal is woven heavily into the technical rounds (the ML system design round, the applied ML deep dive) as well as a dedicated behavioral round. This lesson defines the cross-cutting ML and data signals interviewers grade, walks through how the loop probes for experimentation discipline rather than story-telling about results, maps the signals to the questions interviewers ask, and shows two model answers tailored to the experiment-was-wrong and data-ethics judgement story shapes.
AI Engineering
Design a release gate with slices and uncertainty
Decide whether a candidate with better aggregate success but a regressed protected slice can ship, and design a release gate with calibrated graders, traces, and stated uncertainty.
Design a model specialization program
Design a specialization program for a fictional hint model within GPU-hour and expert-review budgets: choose learning signals and parameterization, protect splits and evaluation, and plan a release with rollback. Then investigate a candidate that won a preference comparison while experts found wrong explanations.
Define a user outcome and baseline
Explain why longer sessions do not justify shipping AI hints, then use a fictional pilot's per-arm figures to propose an evaluation with a real baseline, an independent learning outcome, and guardrails.
Design an A/B test with cost guardrails
Write the experiment contract and sample-size plan for a staged-hints A/B test, implement a validated per-arm summary of outcomes and costs, and explain which checks and guardrails must pass before a lift can justify shipping.
Decide whether to ship on conflicting evidence
Check the validity of a synthetic A/B test of an AI coach, compute its rates, intervals, and costs, and defend a release decision when offline ratings, immediate metrics, and matured outcomes disagree.