Scenario: Model a university domain with Students, Courses, Departments, Instructors, and Enrollments. Tasks: 1) OLTP ERD: Specify normalized tables with PRIMARY/FOREIGN keys and constraints (e.g., a student cannot enroll in two sections of the same course in the same term; grade in {A,B,C,D,F,Pass,Fail,Incomplete}). Include indexes you would create and why. 2) Warehouse: Design a star schema for analytics (FactEnrollment with measures like credits_attempted, credits_earned, points; DimStudent, DimCourse, DimDepartment, DimTerm, DimInstructor). Explain your grain and surrogate keys. Model SCD Type 2 on DimStudent for major changes; show columns (effective_start, effective_end, is_current) and how you would join for point-in-time correctness. 3) Partitioning: Propose partitioning/clustering for FactEnrollment at 100M+ rows. Justify by common query predicates and maintenance. 4) Write SQL: a) List students who took both 'CS101' and 'MATH201' in the same term. b) For the most recent completed term, return the top 3 departments by average GPA (weighted by credits). Break ties deterministically. 5) Briefly compare OLTP vs OLAP design trade-offs in this scenario and when you would prefer snowflake over star.