Ace the AI Engineering Exam 2026 – Transform Your Tech Dreams into Reality!

The appropriate sequence for utilizing a model in machine learning involves several crucial steps that ensure the model's effectiveness and reliability. Initially, cleaning the data is essential because it helps remove inaccuracies, handle missing values, and deal with inconsistencies in the dataset. This step is foundational as the quality of the data directly impacts the performance of the model.

After cleaning the data, the next step is to split the data into training and testing sets. This is vital for developing a model that can generalize well to unseen data. The training set allows the model to learn patterns, while the testing set is reserved for evaluating the model's performance objectively.

Following the splitting of data, the model can be fitted using the training dataset. This fitting process involves applying the machine learning algorithm to learn from the data provided. Once the model has been fitted, the next phase is to evaluate its accuracy or performance using the testing set. This final step is crucial as it indicates how well the model will perform in real-world applications.

Thus, the correct sequence of actions—cleaning the data, splitting it, fitting the model, and then evaluating accuracy—establishes a systematic approach to machine learning that ensures optimal model development and validation.