We study \textsc{Max $k$-Weight CSP}: maximize the satisfied weight of Boolean constraints with nonnegative rational weights by setting at most $k$ variables to $1$. Each constraint uses at most $d$ distinct variables and a relation from a finite Boolean language $Γ$. This includes \textsc{Partial Vertex Cover}, \textsc{Densest $k$-Subgraph}, bounded-frequency \textsc{Maximum Coverage}, and cardinality-constrained \textsc{Max-SAT}. We completely classify the fixed finite languages admitting an efficient parameterized approximation scheme (EPAS) with parameter $k+d$. Only nonempty relations excluding the all-zero tuple matter. For such a relation, a coordinate is a \emph{unit witness} if the tuple with a single $1$ in that coordinate is accepted, and a \emph{control} otherwise. A relation is \emph{Direct} if setting any selected control to $0$, with all other coordinates unchanged, preserves satisfaction. It is \emph{Rooted} if every satisfying tuple has a selected unit witness such that keeping it, setting all other unit witnesses to $0$, and setting any subset of selected controls to $0$ preserves satisfaction. If all such relations are Direct, or all are Rooted, we give a deterministic $(1-\varepsilon)$-approximation for every rational $\varepsilon\in(0,1)$ in time $2^{O(d+k\log(1+dk/\varepsilon))}\ell_{\mathrm{in}}^{O(1)}$, where $\ell_{\mathrm{in}}$ is the total input length. The algorithms are uniform in $d$ and give an EPAS parameterized by $k+d$ whenever relation membership is polynomial-time decidable. If neither condition holds, no EPAS exists unless $\mathsf{FPT}=\mathsf{W[1]}$, even for unweighted instances where no two constraints involve the same set of variables.

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