A data analyst is creating a forecast for a product line introduced early last year.

This question pertains to theData Analysisdomain, focusing on data types and methods for forecasting. The task involves projecting sales (numerical) and customer satisfaction (likely ordinal,e.g., ratings), requiring appropriate data attributes.

Standard deviation and constraints (Option A): Standard deviation measures data spread, and constraints are conditions, neither of which directly supports forecasting.

Mean and median (Option B): Mean and median are descriptive statistics, not sufficient for forecasting future values.

Boolean data and an array (Option C): Boolean data (true/false) and arrays (data structures) are not relevant for forecasting sales and satisfaction.

Numerical and ordinal attributes (Option D): Sales are numerical (e.g., units sold), and customer satisfaction is often ordinal (e.g., 1-5 ratings). These attributes are suitable for forecasting models (e.g., time-series analysis for sales, regression for satisfaction).

The DA0-002 Data Analysis domain includes "applying the appropriate descriptive statistical methods," and numerical and ordinal attributes are key for forecasting sales and satisfaction.