Which solution will meet this requirement?
Add an override to the feature. Set the identifier of the override to the engineer’s user ID. Set the variation to Variation A.
Add an override to the feature. Set the identifier of the override to Variation A. Set the variation to 100%.
Add an experiment to the project. Set the identifier of the experiment to Variation B. Set the variation to 0%.
Add an experiment to the project. Set the identifier of the experiment to the AWS account’s account ISet the variation to Variation A.
Explanations:
Adding an override with the engineer’s user ID will ensure that the application consistently serves Variation A to that user, effectively making it the only visible variation when they access the endpoint. This isolates their experience without affecting other users.
Setting the identifier of the override to Variation A and the variation to 100% is not a valid approach. The identifier should correspond to a user or group, not a variation, to correctly apply the override for specific users.
Adding an experiment with Variation B set to 0% does not achieve the desired outcome of exclusively showing Variation A. This configuration would still leave Variation B in the project, albeit with no traffic, which is unnecessary if the goal is to work solely with Variation A.
Adding an experiment that references the AWS account’s ID and setting the variation to Variation A does not restrict visibility to only Variation A. It doesn’t align with the goal of ensuring Variation A is the only variation presented at the application endpoint.
I categorize that the answer is:
Add an override to the feature. Set the identifier of the override to the engineer’s user ID. Set the variation to Variation A.
I sort that the answer is:
Add an override to the feature. Set the identifier of the override to the engineer’s user ID. Set the variation to Variation A.