The question is testing a basic but important AI distinction: blind (uninformed) search versus heuristic-guided (informed) search. The quickest way to answer is to check each option for whether it uses a heuristic.
- Breadth First Search: expands all nodes at the current depth before moving deeper, purely based on the tree structure. No heuristic is used anywhere, so this is uninformed search.
- Depth First Search: follows one path as deep as it can go before backtracking to try another branch. Again, this decision is based only on structure, not on any estimate of closeness to the goal, so it is uninformed search.
- A* Search: combines the actual path cost so far, $g(n)$, with a heuristic estimate of the remaining cost, $h(n)$, into a total score $f(n) = g(n) + h(n)$, and always expands the node with the lowest $f(n)$ first. Because it leans on that heuristic $h(n)$ to make smarter choices, A* is the odd one out, it is informed search, not uninformed.
- Depth-limited Search: this is just Depth First Search capped at a maximum depth so it does not run forever down one branch. The depth cutoff is a structural limit, not a heuristic estimate of goal distance, so it still counts as uninformed search.
Three of the four options, BFS, DFS, and Depth-limited Search, never look at any estimate of how close a state is to the goal, they just follow the shape of the search tree. Only A* Search brings in a heuristic function to steer the search, which is exactly what separates informed from uninformed methods.
So the one algorithm that is NOT uninformed search is A* Search, option (C).
\[ \boxed{\text{A* Search}} \]