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Given a logistics problem with multiple constraints (fuel, capacity, speed), which of the following is the most likely optimization technique a data scientist would apply?
Correct Answer: A
# This is a classic constrained optimization problem: the boats have fuel, volume, and speed constraints. The goal is to maximize box transport within the fixed limits (e.g., fuel). Constrained optimization methods are explicitly designed to handle such problems. Why other options are incorrect: * B: Unconstrained methods do not account for fuel or capacity limits - inappropriate. * C: Most real-world constrained problems require iterative approaches for convergence. * D: Iterative may be part of solving, but it's not a type of optimization - constrained is the category. Official References: * CompTIA DataX (DY0-001) Study Guide - Section 3.4:"Constrained optimization is used when variables must meet certain limitations or bounds." -
