DSA for Frontend Certification
One paper across the whole course: Big-O reasoning grounded in real frontend scenarios, array and string patterns, hashing, recursion and the call stack, trees including the DOM itself, linked lists and an LRU cache, graphs lightly, sorting and searching, and applying it all to debouncing and list virtualization. Sat in one two-hour sitting.
- Sitting
- 2 hours
- Questions
- 50
- Pass mark
- 70%
- Price
- ₹199$9.99
Sit this when you can do these without looking them up
This is not a certificate of attendance. It is 50 questions in 2 hours at 70% with no partial credit and one attempt, and roughly a third of the paper is foundations — the certificate is for the whole language, not the parts you enjoyed.
- State the time and space complexity of a sliding-window or two-pointer solution and justify both, not just assert them
- Spot a linear method (.includes(), .indexOf(), .find()) called inside a loop, and explain why swapping it for a Set or Map changes the overall complexity class
- Explain why array.push() is called amortized O(1) despite an occasional O(n) resize, and derive merge sort's O(n log n) from its recursion depth and per-level work
- Trace a recursive function's call stack by hand, and say why the same code can work in every test but overflow on real, deeply nested production data
- Choose between BFS and DFS for a tree or graph problem and justify it — not just name both
- Design an LRU cache's get/put/evict using a map and a doubly linked list, and explain why neither structure alone gets all three operations to O(1)
- Explain why debounce and throttle are different sliding-window shapes, and why virtualizing a list is a complexity-mismatch problem, not a rendering trick
If several of those are unfamiliar, the course below is free and covers all of it. If you would rather find out than guess, there is a practice paper at the same level — ten questions, marked as you go, no account, nothing recorded.
Take the free practice paperSyllabus
Every paper is drawn to this mix, so the weights are a promise rather than a guide. The numbers are how many of the 50 questions come from each area.
- 3
Big-O reasoning for frontend code
Growth rate versus absolute speed, spotting a linear method called inside a loop, and why the same Big-O can still matter differently depending on real-world n.
- 4
Time complexity, amortized cost, and space complexity
Deriving complexity by counting operations, why array.push() is amortized O(1), auxiliary space versus input size, and the call stack as real memory.
- 3
Two pointers and sliding window
Opposite-ends and same-direction two pointers, fixed- and variable-size sliding windows, and why both stay linear despite looking nested.
- 3
In-place manipulation, prefix sums, and string parsing
Read/write pointer compaction, the splice-while-iterating bug, prefix sums for O(1) range queries, and stacks for balanced-bracket and tokenizing problems.
- 4
Hash maps and sets for O(1) lookup
Why Set.has() beats Array.includes() inside a loop, average versus worst-case hashing, and the reference-versus-value gotcha with object keys.
- 4
Frequency counting, two sum, and grouping with maps
One-pass frequency counting, the two-sum complement pattern, and map-based grouping and deduplication by a chosen key.
- 4
Recursion and the call stack
Base cases and recursive cases, tracing frames by hand, and why a recursive result resolves from the base case outward as the stack unwinds.
- 3
Recursion vs. iteration, memoization, and divide and conquer
Stack overflow risk on real nested data, the tail-call-optimization myth in non-Safari engines, memoization turning exponential into linear, and merge sort's divide-and-conquer proof.
- 4
Binary trees and traversal (BFS/DFS)
Tree vocabulary, the binary search tree ordering property and its balance-dependent O(log n) promise, and choosing between BFS and DFS.
- 4
The DOM as a tree and real frontend tree problems
querySelectorAll and closest() as named traversals with real complexity, flattening nested comments, building a file tree, and searching a component tree.
- 3
Linked lists, stacks, and queues
O(1) insertion trade-offs versus arrays, undo/redo as a stack, browser navigation as two stacks, and the shift() gotcha in a naive queue.
- 2
The LRU cache
Combining a map and a doubly linked list to get get/put/evict all to genuine O(1), and why neither structure alone achieves it.
- 3
Graphs, lightly
Adjacency lists versus matrices, the visited set graphs need that trees don't, cycle detection with three-color DFS, and topological sort.
- 3
Sorting and binary search
Array.prototype.sort's real complexity and stability, the no-comparator numeric-sort bug, binary search's proof, and the search-on-answer pattern.
- 3
Applying DSA to real frontend problems
Debounce and throttle as sliding-window shapes, and list virtualization as a complexity mismatch solved with prefix sums and binary search together.
Everything on this syllabus is taught free
The DSA for Frontend course covers the whole syllabus above across 30 lessons, with a playground in each one. Work through it first — the exam is the same material under a clock.
Open the DSA for Frontend courseWhat sitting it is like
The exam opens full screen with a countdown, a question at a time, and a grid showing what you have answered and what you have flagged to come back to. Nothing is submitted until you say so, or the clock reaches zero.
Every answer saves to the server as you give it. If your laptop dies at question forty, sign back in and you will find question forty exactly as you left it — with however much time was left still ticking, because the clock does not pause.
Leaving full screen or switching tabs is noted but does not end your exam. It is a record, not a trap: nobody is auto-failed for a notification stealing focus.
When it ends you get your mark, a topic-by-topic breakdown, and every question with the right answer and an explanation — whether you passed or not. Passing also produces a certificate with a link anyone can verify.