Splunk Core Certified User Practice Exam

The best description of time-series data is when it refers to any data that includes timestamps. Time-series data is characterized by its ability to track changes over time, which is essential for various types of analysis, such as monitoring trends, seasonality, and correlations across different time intervals. Each data point in a time-series dataset is typically associated with a specific point in time, allowing for dynamic and temporal analysis that can be visualized as a series of observations over a specified duration.

In contrast, data that varies with geographical locations pertains more to spatial data, while data that lacks a timestamp cannot be classified as time-series because timestamps are essential to provide the temporal aspect. Lastly, purely categorical data does not involve time dependencies and is not suitable for time-series analysis. These differentiations illustrate why the inclusion of timestamps is central to the characterization of time-series data.