Review & Mastery Checks
Learning a topic once is acquisition. Remembering it three months later, in a 3-hour exam that mixes every subject, is retention — and that takes a different kind of practice. The checks below interleave questions from across a whole subject, out of lesson order, because mixing topics is what builds durable recall and the ability to switch gears mid-paper. Every question reuses an answer verified against the official 2024–2026 papers.
How to use this page
Don't take these the day you finish a subject — that just re-tests what's still in working memory. Space them out. Each time you successfully recall a topic after partly forgetting it, the memory gets stronger and lasts longer. A simple, research-backed schedule:
- • Right after a subject: do its per-lesson quizzes (you already have these).
- • About a week later: take that subject's mastery check below — cold, no notes.
- • A month on: retake it and write down every question you miss. Those misses are your real syllabus — go re-read just those lesson sections.
- • Mock season (week ~10+): cycle all four, then move to full timed mocks.
Mastery checks for the four heaviest subjects are below. (Calculus, Databases, AI, and General Aptitude have full per-lesson quizzes in their roadmaps; mixed checks for them are on the way.)
Probability & Statistics
Interleaves Bayes, independence, distributions, covariance, CLT, and descriptive stats.
Probability — interleaved mastery check
Machine Learning
Mixes naive Bayes, neural nets, clustering, PCA, ridge, metrics, SVM, and regression.
Machine Learning — interleaved mastery check
Linear Algebra
Mixes eigenvalues, SVD, determinants, projections, quadratic forms, orthogonality, and rank.
Linear Algebra — interleaved mastery check
Programming, Data Structures & Algorithms
Mixes Python gotchas, binary search, sorting, quicksort, hashing, and tree traversal.