MML-06-02: Multivariable Functions, Partial Derivatives & The Gradient Vector – Practice Quiz

35 worked single-select and multiselect questions. Tier 1–3. Prerequisites: QS 6.1, Part 02. Learning goals: Distinguish scalar-valued functions from vector-valued mappings; Compute partial derivatives while holding other coordinates fixed; Assemble and evaluate a column gradient; Use gradient/Jacobian layout conventions consistently; Interpret gradient fields and critical points on standard surfaces; Diagnose coordinate-wise sensitivity in small ML loss examples; Recognize why partial derivatives alone do not guarantee differentiability. Column gradients and output-by-input Jacobians are used where relevant. Detailed solutions explain every choice. References: Source 1, Source 2, Source 3.