Tesla Interview Questions

This asks whether a string's characters can be rearranged so no two adjacent characters are the same, and if so, to produce one such arrangement. The standard approach counts character frequencies, checks the feasibility condition (no character exceeds (n+1)/2 occurrences), and then greedily places the most frequent remaining characters using a max-heap. It tests the combination of a feasibility argument with a greedy, heap-driven construction, rather than pure search.

Given an array and a target, find the two indices whose values sum to the target, typically solved in a single pass using a hash map that stores each value's complement as it's seen. It requires no advanced technique, which is exactly the point — it's a fast, low-friction warm-up used to confirm a candidate can reach for a hash map instead of defaulting to a brute-force O(n^2) nested loop.

Given a list of strings, group the ones that are anagrams of each other. The typical solution hashes each string to a canonical key (either its sorted characters or a fixed-length character-count signature) and groups strings sharing a key in a hash map. It's a straightforward but reliable test of whether a candidate reaches for the right canonicalization strategy and understands the tradeoffs between sorting-based and counting-based keys.

Find all unique triplets in an array that sum to zero. The standard approach sorts the array, then for each element uses a two-pointer sweep over the remaining elements, carefully skipping duplicate values to avoid repeated triplets in the output. It extends the simpler two-pointer/two-sum pattern into a triplet setting and tests whether a candidate can handle the deduplication logic correctly, which is where most candidates lose points.

Find the contiguous subarray with the largest sum, the textbook application of Kadane's algorithm: track the best sum ending at the current index (either extend the previous subarray or start fresh at the current element) alongside a running global maximum. It's one of the most fundamental dynamic-programming-style problems in interviewing, testing whether a candidate has the core DP intuition of 'best solution ending here' down cold.

Count the number of contiguous subarrays that sum to exactly k. The efficient solution uses a running prefix sum alongside a hash map counting how many times each prefix-sum value has occurred, since a subarray sums to k exactly when the difference between two prefix sums equals k. It's a strong test of the prefix-sum-plus-hash-map pattern, a technique that generalizes to many other subarray-counting problems.

A prefix-sum problem: find an index where the sum of all elements to its left equals the sum of all elements to its right. The efficient approach computes the total array sum once, then scans left to right maintaining a running left-sum and deriving the right-sum by subtraction instead of recomputing it every time. It's a quick check on whether a candidate reaches for prefix sums instead of a brute-force O(n^2) sum-both-sides-per-index approach.

A binary-search problem: find a target value's index in a rotated sorted array in O(log n) time. It tests whether a candidate can identify which half of the array around the midpoint is properly sorted at each step, then decide whether the target falls within that sorted half or the other one. Its wide reach across companies reflects how well it isolates genuine binary-search intuition from candidates who only know the textbook sorted-array version.

Word Ladder models words as nodes in an implicit graph, connecting two words if they differ by exactly one letter, and asks for the shortest transformation sequence from a start word to an end word via BFS. Because the graph isn't given explicitly, candidates must generate neighboring words on the fly (e.g., by trying all 26 letter substitutions at each position) and use a word dictionary/set for O(1) membership checks and visited tracking. Its practical relevance in interviews often ties to NLP transformations — reasoning about minimal edit-style paths between text tokens — which is one reason it recurs in ML-adjacent interview loops.

A Hard-tier array problem solved with an in-place, index-as-hash-table technique: you place each positive number ≤ n at its correct index (value v goes to index v-1) via swaps, then scan for the first index whose value doesn't match, all within O(n) time and O(1) extra space. It tests whether a candidate can find a genuinely non-obvious optimal solution rather than the natural but disallowed approach (a hash set), since the O(1)-space constraint is the entire point of the problem. It's a strong signal for algorithmic creativity under a tight space constraint, commonly used to differentiate strong candidates in a Hard-tier slot.

Find the smallest substring of a string s that contains every character of another string t (respecting character multiplicities). The solution is a variable-size sliding window with a hash map of required character counts and a counter tracking how many distinct required characters are currently satisfied, expanding the right pointer until the window is valid and then greedily shrinking from the left. It's a step up from basic sliding-window problems because of the 'expand until valid, then shrink while still valid' two-phase logic, which trips up candidates who haven't internalized the pattern.

Given a list of words sorted according to an unknown alphabet's ordering, the task is to reconstruct a valid character ordering: build a directed graph of precedence constraints from adjacent word comparisons, then topologically sort it (Kahn's algorithm or DFS-based), detecting cycles as an invalid/impossible ordering. The task is conceptually similar to inferring a token ordering from partial, pairwise evidence, which is plausibly why it resonates with companies doing tokenizer/vocabulary-adjacent ML work. It's a solid test of translating a word-comparison problem into a graph and correctly handling topological sort edge cases (cycles, ties, unreachable characters).

Given a set of 2D points, find the minimum-area axis-aligned rectangle that can be formed using four of them as corners. The standard approach hashes points by column (or stores all points in a set) and checks pairs of points sharing the same x-coordinates as potential right-left edges, verifying the other two corners exist and tracking the minimum area. It's a solid signal for geometric reasoning combined with efficient hashing/lookups instead of a naive O(n^4) corner search.

Despite the array being unsorted, a peak (an element greater than its neighbors) can still be found in O(log n) using binary search by comparing the midpoint to its neighbor and moving toward the side that's still increasing. It's a good test of whether a candidate understands why binary search applies here even without global sortedness -- the key insight is that the search space always contains a peak in the direction of the ascending neighbor. This is often used to probe binary-search intuition beyond the standard "find target in sorted array" template.

Given a string with words separated by variable amounts of whitespace, reverse the order of the words while collapsing extra spaces down to single spaces and trimming leading/trailing whitespace. Beyond the core word-reversal logic, the whitespace-normalization edge cases are usually where solutions break, making this a decent test of careful string handling. A common follow-up asks for an in-place solution using O(1) extra space, which pushes toward a two-pointer/in-place-reversal technique instead of just splitting and rejoining.

Determine whether any permutation of a given string could form a palindrome, which reduces to a parity check: a string can be rearranged into a palindrome if and only if at most one character has an odd count. The clean solution uses a hash map or a bitmask (toggling a bit per character) to track parity in a single pass. It's a short, low-complexity problem mainly testing whether a candidate recognizes the parity-based characterization instead of trying to actually generate permutations.

Checks whether a singly linked list reads the same forward and backward, typically solved by using slow/fast pointers to find the middle, reversing the second half in place, and comparing it against the first half. Doing this in O(1) extra space (rather than copying values into an array) is usually the bar interviewers expect, since it combines three separate linked-list techniques — middle-finding, in-place reversal, and two-pointer comparison — into one solution. It's a good signal for whether a candidate can compose multiple linked-list primitives correctly rather than just knowing each one in isolation.

Given a list of words, return the k most frequent ones, with ties broken by lexicographic order. It combines a frequency hash map with either a heap using a custom comparator for the tie-breaking rule, or a bucket-sort-by-frequency approach for an O(n) alternative. The tie-breaking requirement is the real trap — many otherwise-correct solutions get the right frequencies but the wrong order among equally-frequent words, making this a good test of attention to problem-statement detail, not just knowledge of heaps.

This problem can be solved with a fixed-size min-heap of size k (keeping the k largest elements seen so far) for O(n log k), or with quickselect — a partition-based approach related to quicksort — for expected O(n). It's frequently used to gauge whether a candidate knows more than one approach and can reason about the time/space tradeoffs between a heap-based streaming solution and an in-place partitioning one. Handling duplicate values and off-by-one indexing during partition are common places candidates slip up.

Given tasks with a cooldown period between identical tasks, this problem asks for the minimum total time (including idle slots) to complete them all. It can be solved with a closed-form formula based on the most frequent task's count and the number of tasks tied for that frequency, or simulated with a greedy max-heap approach. It tests whether a candidate can derive and justify a mathematical bound rather than only brute-force simulating the schedule.

This is the classic Dutch National Flag problem: sort an array containing only three distinct values (commonly 0, 1, 2) in a single pass using three pointers (low, mid, high) that partition the array into three regions as they scan. It tests whether a candidate can implement in-place three-way partitioning correctly, including the subtlety of not advancing the mid pointer after a swap with the high pointer. It's a frequently reused building block in quicksort-style partitioning, so it also signals whether that broader pattern is understood.

Rotate an n x n matrix 90 degrees clockwise in place. The common technique is to transpose the matrix (swap elements across the diagonal) and then reverse each row, though a layer-by-layer four-way swap also works. It's a compact test of in-place matrix manipulation and geometric/index reasoning without needing any extra data structure.

A classic topological-sort problem: given courses and prerequisite pairs, return a valid ordering (or detect that no valid ordering exists due to a cycle). It tests whether a candidate can build an adjacency list, track in-degrees, and run Kahn's BFS algorithm (or DFS with visited/in-progress marking) to order and cycle-detect simultaneously. Its broad company reach reflects how often real systems need this exact shape of problem — resolving build or package dependency order, or task scheduling graphs — making it a reliable signal for graph traversal fluency.

Merging k sorted linked lists efficiently requires either a min-heap holding the current head of each list (repeatedly popping the smallest and pushing its successor) or a divide-and-conquer pairwise merge, both achieving O(N log k). It tests whether a candidate can extend the two-list merge pattern to k lists and reason about the resulting complexity rather than defaulting to an O(Nk) linear scan across lists. Correct pointer management across multiple linked lists under time pressure is the practical difficulty.

Given an elevation map, compute how much water it can trap after rain — the water trapped at each index equals the shorter of the tallest bars to its left and right, minus its own height. It's solvable with two pointers in O(1) extra space, with a monotonic stack, or with two precomputed max-prefix/max-suffix arrays, making it a great vehicle for comparing multiple valid techniques in one interview. Its breadth of companies asking it reflects that it's one of the most widely used checks of two-pointer/array reasoning in the entire interview circuit.

A design problem requiring O(1) get and put operations with least-recently-used eviction, implemented by combining a hash map (for O(1) lookup) with a doubly linked list (for O(1) reordering and eviction from either end). This exact structure underlies real inference key-value caches — it's reportedly one of the most frequently asked design questions at companies building LLM inference systems, since eviction policy for a bounded cache maps directly onto managing GPU memory for attention KV caches. It's a strong test of whether a candidate can compose two data structures to get O(1) across every required operation, not just some of them.

A foundational data-structure design problem: implement insert, search, and startsWith operations for a prefix tree, typically using nested hash maps or fixed-size child arrays per node. It tests whether a candidate understands how tries achieve O(L) lookups (L = word length) independent of dictionary size, and can reason cleanly about node structure and end-of-word marking. It's a common warm-up before harder trie-based follow-ups like wildcard search or autocomplete, and is frequently used to gauge comfort with recursive tree-like structures.

A design problem: encode a binary tree into a string and reconstruct an identical tree from that string, typically via a preorder traversal with explicit null markers, or a BFS-based level encoding. The candidate must design a format that's unambiguous enough for deserialization to rebuild structure without extra information. Interview relevance frequently maps to data persistence — serializing structured data for storage or transmission and reliably reconstructing it — which is why it shows up across companies handling tree-like or hierarchical data on disk or over the wire.

A from-scratch data structure design problem: implement a hash map's put/get/remove without using any built-in map type. The typical solution allocates a fixed-size array of buckets, each holding a small linked list (or array) of key-value pairs for separate chaining, with a simple hash function mapping keys to bucket indices. It's a foundational test of whether a candidate actually understands how the hash maps they use every day work internally, including collision handling.

A design problem simulating a subway system: implement checkIn/checkOut calls that record when a passenger enters and exits a station, and an averageTime query that returns the average travel time between two specific stations across all completed trips. The clean solution uses one hash map keyed by passenger ID to hold in-progress trips and a second map keyed by (start station, end station) to accumulate total time and trip count for O(1) average lookups. It tests whether a candidate can decompose a stateful real-world system into the right set of hash maps rather than re-scanning trip history on every query.

This design problem asks for a counter that records hits with timestamps and can report the number of hits in the past 300 seconds. It's typically implemented with a queue (or a fixed-size circular buffer of timestamp/count pairs) that evicts entries falling outside the trailing time window as new hits arrive. It tests whether a candidate can design a data structure for a sliding time window under both single-hit and (in a common follow-up) batched-hit conditions.

Counts the number of set bits (1s) in the binary representation of an unsigned integer, i.e., its Hamming weight. The standard efficient technique repeatedly clears the lowest set bit via n & (n - 1) instead of checking all 32 bits individually, cutting the loop count down to the number of set bits. It's a short bit-manipulation warm-up mainly testing familiarity with common bit tricks.

Reverses the bits of a given 32-bit unsigned integer, typically by shifting the result left and pulling the input's lowest bit off with each iteration. It's a compact test of low-level bit manipulation and careful handling of fixed-width integer operations, and is often paired with Number of 1 Bits and similar problems as a batch of quick bitwise-fluency checks in a single interview.

Given an array where every element appears twice except one, find the element that appears once — solved in O(n) time and O(1) space by XOR-ing every element together, since a value XORed with itself cancels to zero, leaving only the unpaired element. It's a small but elegant problem that tests whether a candidate knows this specific XOR property rather than defaulting to a hash-set-based approach that uses extra space.

For every number from 0 to n, compute the number of set bits in its binary representation, using dynamic programming so each answer is derived from a previously computed smaller value (e.g., bits[i] = bits[i >> 1] + (i & 1)) instead of recounting bits from scratch each time. It's a good test of recognizing a bit-manipulation recurrence and building a DP table from it, a slightly different flavor from the more common array or string DP problems.

Given an array of n+1 integers where each value is between 1 and n, find the single duplicate value without modifying the array and using only O(1) extra space. The intended solution treats the array as an implicit linked list (index i points to index array[i]) and applies Floyd's cycle-detection algorithm to find the cycle's entry point, which corresponds to the duplicate. It's a strong signal for whether a candidate can recognize a disguised linked-list-cycle problem underneath what initially looks like a pure array problem.

Computes the sum of two integers without using the + or - operators, implemented instead with bitwise XOR (to add without carrying) and AND-then-shift (to compute the carry), repeated until there's no carry left to propagate. It's a low-level bit-manipulation problem that also tests awareness of language-specific integer overflow and sign-bit handling, since the carry-propagation loop can behave differently across languages with fixed-width versus arbitrary-precision integers.