CompTIA DataX DY0-001 (V1) Practice Question

The correct answer is a box and whisker plot. A box plot is the most effective tool for this scenario because it is specifically designed to summarize and compare the distributions of a continuous variable across multiple groups or categories. It concisely displays key statistical measures for each service: the median (central tendency), the interquartile range (IQR) representing the middle 50% of the data (variability), and the whiskers and individual points beyond them (outliers). This makes it highly efficient for comparing hundreds of service distributions at a glance to identify those with high spread (a long box or whiskers) and numerous outliers.

A histogram is not ideal because it would require generating hundreds of individual plots, one for each microservice. Comparing these many plots side-by-side would be impractical and inefficient for identifying services with high variability and outliers.

A scatter plot is used to visualize the relationship between two continuous variables. Using it to plot a continuous variable (response time) against a categorical one (service ID) would result in a series of vertical dot strips that would be heavily overplotted and difficult to interpret, especially with hundreds of services.

A Q-Q plot is used to determine if a dataset follows a specific theoretical distribution, like a normal distribution. It is not designed for comparing the summary statistics of distributions across many different groups. The data scientist would need to create a separate plot for each of the hundreds of services to assess their individual distributional shapes, which does not meet the goal of an efficient, comparative analysis.