A time series profile repeatedly calculates data-quality metrics as a table changes and preserves those measurements over time. This makes it possible to observe whether the number of null values in each column is increasing, decreasing, or remaining stable as new records are added. A snapshot profile evaluates the table at a particular point in time and is therefore insufficient when the requirement is to analyze metric progression across multiple updates. Inference is associated with deriving information such as a schema or statistical characteristics; it is not the profile type used to maintain a historical sequence of monitoring measurements. Because the question requires repeated evaluation and the ability to observe changes throughout the dataset’s progression, the time series profile satisfies both requirements.