Step 1: List the fixed and variable credit components. Let Core = 15 credits (fixed, compulsory), Project = 20 credits (fixed, compulsory), Specialization = S credits (variable, with S >= 10), and Elective = E credits (variable, the quantity to maximize). The total enrolled credits T = Core + Project + S + E = 15 + 20 + S + E = 35 + S + E.
Step 2: Write down the given constraints. The total credits must satisfy 60 <= T <= 70 (minimum 60, maximum 70). The specialization credits must satisfy S >= 10 (minimum 10, with no stated upper cap on specialization alone). We need to find the maximum possible value of E.
Step 3: Express E in terms of T and S. From T = 35 + S + E, we get E = T - 35 - S.
Step 4: Maximize E by choosing T and S optimally. To make E as large as possible, we want T as large as possible and S as small as possible, since E decreases as either T decreases or S increases. The largest allowed T is 70 (the upper credit cap), and the smallest allowed S is 10 (the specialization minimum). Substituting these extreme values: E_max = 70 - 35 - 10 = 25.
Step 5: Verify the resulting allocation is feasible. With Core = 15, Project = 20, Specialization = 10, Elective = 25, the total is 15 + 20 + 10 + 25 = 70, which lies exactly at the upper bound of the allowed range 60 <= T <= 70, so it is a valid enrollment. The specialization value of 10 also meets its minimum requirement of S >= 10. All constraints are satisfied simultaneously, confirming that E = 25 is achievable, not just an unreachable upper bound.
Step 6: Rule out the other options. Option (A) 10, option (B) 15, and option (C) 20 are all smaller than 25 and therefore not the maximum - the student could enroll for more elective credits than these values while still staying within the rules, so they understate the true maximum.
\[ \boxed{25} \]