How do you prevent concurrent onboarding and navigation experiments from polluting results?

WHAT THIS TESTS: The interviewer wants to know if you understand that running multiple experiments on the same user population creates the risk of interaction effects, where the outcome of one experiment is confounded by another. They are looking for familiarity with the concept of mutually exclusive layers, which is the industry standard approach for isolating concurrent experiments. This is fundamentally about experimental design and traffic allocation mechanics, not just feature flagging.

A GOOD ANSWER COVERS: First, explain that users must be consistently bucketed using a deterministic hash of a stable identifier like user ID so the same user always lands in the same bucket. Second, define a layer as an isolated allocation plane where a user is assigned to exactly one variant within that layer. Third, describe placing each experiment into its own layer, meaning a user can be in variant B of the onboarding layer and variant A of the navigation layer independently because the layers use separate hash salts or seeds. Fourth, note that if the experiments are suspected to strongly interact, you should instead put them into the same layer so they are mutually exclusive, forcing a user into only one of the two experiments. Fifth, mention that the hashing and layer assignment must happen upstream before any variant logic executes.

COMMON WRONG ANSWERS: A major red flag is suggesting simple random assignment per experiment without any shared bucketing system, which guarantees user overlap and makes results uninterpretable. Another error is proposing to analyze interactions post-hoc without preventing the overlap during assignment, since statistical power for interaction terms drops drastically and business decisions become noisy. Some candidates incorrectly suggest time-based separation as the primary solution, which serializes learning and ignores that the question asks for concurrent execution. Finally, confusing layers with segments is a warning sign; segments filter who enters an experiment, while layers determine which variant a user sees.

LIKELY FOLLOW-UPS: The interviewer may ask how you would handle three or more concurrent experiments and whether you would put them all in separate layers or group some together. They might also ask what you would do if two experiments must be mutually exclusive because they touch the same UI surface, or how you maintain session consistency if a user switches devices. Another common follow-up is asking how you would measure an interaction effect if you suspect one exists despite layer separation.

ONE CONCRETE EXAMPLE: Imagine you have one hundred thousand daily active users. You create two layers. Layer one uses a hash of user ID plus the salt onboarding_layer to assign each user to either control or new onboarding flow. Layer two uses a hash of user ID plus the salt nav_layer to assign each user to either control or new navigation bar. Because the salts differ, the bucket assignments are uncorrelated. A user hashed to variant A in layer one and variant B in layer two experiences both changes independently. If you later discover that the new onboarding plus new navigation together cause a drop in retention, you can detect this via an interaction analysis without either experiment polluting the other's primary metric.

Source: launchdarkly.com