A consultant builds a custom AI Builder prediction model to score leads based on the client's unique qualification criteria and successfully trains it in a sandbox environment. When the consultant tries to publish the model to the production environment, the operation fails with a capacity error. What is the most likely configuration gap?
Select an answer to reveal the explanation.
Short Explanation
Training a custom model in a sandbox is the easy part, publishing it into a live environment is where it actually has to run, and running costs a resource the sandbox testing didn't need much of: AI Builder capacity. A production environment that hasn't been allocated enough of that service capacity will let the model exist but refuse to actually execute it, and that shows up as a capacity error at publish time, not a permissions error or a missing-field error. Rebuilding the model from scratch directly in production isn't necessary, since training somewhere and publishing elsewhere is the normal, supported pattern. It's also not about whether the lead entity has the built-in predictive-scoring fields, because a custom model trained on the client's own criteria isn't relying on those fields at all, it was built around whatever inputs the consultant chose. And a capacity error is a distinctly different signal from an access-denied error, so chasing a security role change here would be solving the wrong problem entirely.
Full Explanation
The correct answer is C. Publishing an AI Builder model to run in a given environment requires that environment to have sufficient AI Builder service capacity allocated to it; a sandbox used purely for training and testing may not have been provisioned with the same capacity as production. When the model is published to production without adequate capacity, the operation fails specifically with a capacity error, which matches what is described. The fix is to allocate or purchase additional AI Builder capacity for the production environment. Option A is incorrect because training a model in one environment and publishing it to another is a normal, supported workflow; nothing requires rebuilding the model directly in production. Option B is incorrect because a custom AI Builder model trained on the client's own qualification criteria is built around whatever fields the consultant selected during training, not the out-of-the-box predictive lead scoring fields, so their absence would not cause this failure. Option D is incorrect because insufficient privileges typically surface as an access-denied or permissions error rather than a capacity error, and the scenario explicitly describes a capacity failure.