How can a dataset be distinctly created in TensorFlow?

Creating a dataset in TensorFlow can be achieved through both data sources and data transformations, which is why the selection indicating that both methods can construct a dataset is correct.

A data source, such as loading data from in-memory arrays or files, allows for the direct creation of datasets. TensorFlow's tf.data.Dataset.from_tensor_slices() method, for example, can take in data from Python lists or NumPy arrays, enabling users to integrate data conveniently into the TensorFlow environment.

On the other hand, a data transformation applies operations to existing dataset objects to create new datasets. Through methods like map(), filter(), and batch(), one can modify datasets derived from original sources, effectively tailoring them for specific machine learning tasks.

The capability to create a dataset through either of these means enhances the flexibility and usability of TensorFlow in handling various data scenarios. This versatility is essential for building robust machine learning workflows.