Generative AI Leader Certification Practice Test 2025 – Complete Exam Prep

The activity that incurs costs when building Generative AI solutions primarily involves training the model and deploying it to an endpoint. This process is resource-intensive and necessitates significant computational power, storage, and time. Training a model involves processing vast amounts of data, which can be costly depending on the data's complexity and the model's architecture. Additionally, once the model is trained, deploying it to a production environment requires infrastructure, maintenance, and possibly further tuning, all of which contribute to ongoing expenses.

Formulating business strategies and conducting market research, while essential steps in the overall process of developing AI solutions, do not directly involve the technical and operational costs associated with the actual development and implementation of the model. Hiring AI specialists, although potentially costly, is more of an investment in human resources rather than a direct cost incurred in the technology's deployment and operation.

Thus, training the model and deploying it to an endpoint stands out as the activity that specifically entails the financial investment tied to the technical aspects of building Generative AI solutions.