Different data types require different encoding strategies. The Feedback column contains long, unstructured text responses. According to AWS ML documentation, text data must first be converted into tokens before it can be vectorized using techniques such as embeddings or bag-of-words. Tokenization is the correct preprocessing step for textual features.

The Satisfaction Level column is categorical but has a natural ordering (Low < Medium < High). AWS best practices recommend ordinal encoding for such ordered categorical variables because it preserves the inherent ranking information.

Option A is incorrect because one-hot encoding is not suitable for free-form text and would create an unmanageable number of features. Option B has the same issue for the Feedback column. Option C incorrectly applies label encoding to text and binary encoding to a three-class ordinal variable.

Therefore, tokenization for text data and ordinal encoding for satisfaction levels is the correct solution.

The requirement is to quickly determine whether the target variable is predictable, with minimal development effort. Amazon SageMaker Autopilot is specifically designed for this purpose.

SageMaker Autopilot automatically handles data preprocessing, feature engineering, algorithm selection, model training, and evaluation. It generates multiple candidate models and provides detailed performance metrics, allowing teams to quickly assess predictability without writing custom code.

Option B requires significant manual development. Option C is incorrect because Amazon Macie is a data security and classification service, not an ML modeling service. Option D is unsuitable because Amazon Bedrock models are not intended for structured tabular prediction tasks.

Therefore, SageMaker Autopilot provides the fastest and least effort solution.