What is the correct way to integrate a machine learning model into an Oracle Analytics Cloud project?

Integrating a machine learning model into an Oracle Analytics Cloud project involves the creation of a scenario that utilizes the required model. Scenarios allow users to define various workflows and interactions with data, enabling advanced analyses and predictions based on the outputs of the machine learning model. By choosing this approach, you are effectively leveraging the model's capabilities within the context of your analytics project, allowing for iterative analysis and decision-making based on the insights generated.

This integration method also supports the seamless combination of data manipulation, analysis, and visualization, ensuring that insights derived from the model are readily accessible and actionable. Scenarios serve as the framework within which the model can be operationalized, facilitating tasks like running predictions or performing simulations based on the model outcomes.

The other options do not fully provide the necessary framework for integrating a machine learning model in a robust manner. While custom calculations, visualizations, or data sequences each have their utility within the Oracle Analytics environment, they do not encompass the complete integration and operationalization of a machine learning model as effectively as creating a scenario does.