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A questão aborda o tema de processamento numérico utilizando a linguagem de programação Python e, mais especificamente, sobre módulos especializados para operações avançadas com matrizes, incluindo suas decomposições. O entendimento dos pacotes disponíveis no ecossistema Python para computação científica é fundamental para selecionar a alternativa correta.
O módulo numpy.linalg é parte da biblioteca NumPy, que é uma das bibliotecas mais populares de Python para computação numérica. Este módulo específico fornece um conjunto de rotinas para operações de álgebra linear, como solução de sistemas de equações lineares, cálculo de valores e vetores próprios, e, claro, decomposição de matrizes, como decomposição LU, descomposição QR e decomposição de valores singulares (SVD). Portanto, a alternativa C é a correta pois se refere ao módulo diretamente relacionado com as funções de decomposição de matrizes.
As outras alternativas abordam diferentes módulos que também fazem parte do ecossistema de computação científica do Python, mas não são focados na decomposição de matrizes:
numpy.fftpack – Este módulo é utilizado para cálculos de Transformada Rápida de Fourier (FFT), não para decomposição de matrizes.
scipy.signal – Este módulo é voltado para processamento de sinais e não para álgebra linear.
scipy.optimize – Este módulo oferece funções para otimização e busca de raízes, não estando relacionado diretamente com decomposição de matrizes.
Portanto, ao lembrar que numpy.linalg é o módulo especializado para operações de álgebra linear, você poderá identificar rapidamente a resposta correta para questões similares sobre decomposição de matrizes em Python.