CompTIA DataX DY0-001 (V1) Practice Question

A data‐science team is tuning a pricing engine whose objective is twice-differentiable and non-convex, subject to hundreds of inequality constraints and simple bounds. They have analytic gradients and Hessians and want every iterate to remain strictly inside the feasible region throughout the search. To do this, they choose a solver that

  1. augments the objective with a logarithmic barrier term −μ ∑log sᵢ(x) to prevent boundary violations,
  2. follows a central path by gradually decreasing the barrier parameter μ→0, and
  3. at each outer iteration solves a primal-dual Newton system instead of a quadratic programming subproblem.

Which class of constrained nonlinear optimization algorithms matches this strategy?

  • Sequential quadratic programming

  • Augmented Lagrangian (method of multipliers)

  • Primal-dual interior-point (path-following) method

  • Nelder-Mead simplex search

CompTIA DataX DY0-001 (V1)
Specialized Applications of Data Science
Your Score:
Settings & Objectives
Random Mixed
Questions are selected randomly from all chosen topics, with a preference for those you haven’t seen before. You may see several questions from the same objective or domain in a row.
Rotate by Objective
Questions cycle through each objective or domain in turn, helping you avoid long streaks of questions from the same area. You may see some repeat questions, but the distribution will be more balanced across topics.

Check or uncheck an objective to set which questions you will receive.

SAVE $64
$529.00 $465.00
Bash, the Crucial Exams Chat Bot
AI Bot