Randomized algorithms are algorithms that use randomness as part of their logic to improve performance, simplify implementation, or avoid worst‑case scenarios. Instead of always following a fixed deterministic path, these algorithms make random choices during execution—often leading to faster average performance. Classic examples include randomized quickselect, random shuffling, and probabilistic data structures.
In coding interviews, randomized techniques appear in problems that require uniform sampling, random selection, or probabilistic guarantees. Companies like Google, Meta, and Amazon frequently test candidates on these ideas because they demonstrate deeper understanding of algorithm design and probability. Problems such as picking a random element from a data stream or selecting a random index efficiently often combine randomness with structures like Array or Hash Table.
There are several common patterns used in randomized DSA problems:
You should consider randomized techniques when deterministic solutions are complex, when you need uniform distribution of outcomes, or when avoiding worst-case inputs is important. Many randomized algorithms have excellent expected time complexity while remaining simple to implement.
FleetCode provides 12 carefully selected Randomized practice problems designed to build these skills step by step. By solving them, you’ll learn how to design algorithms with randomness, analyze expected complexity, and confidently handle interview questions that involve sampling, shuffling, and probabilistic guarantees.
Many randomized problems involve selecting or shuffling elements within arrays. Knowledge of indexing, in-place operations, and iteration is essential for implementing efficient randomized solutions.
Hash tables are often used alongside randomness to map values, track frequencies, or simulate uniform selection across elements.
Quickselect demonstrates how random pivot selection leads to expected O(n) performance. Learning it builds intuition for randomized partitioning techniques.
Several randomized algorithms use divide-and-conquer structures where random choices help balance partitions and avoid worst-case scenarios.
Randomized algorithms rely on probability concepts such as expected value, uniform distribution, and independence. Understanding these ideas helps analyze why randomized approaches achieve good average-case performance.
| Status | Title | Solution | Practice | Difficulty | Companies | Topics |
|---|---|---|---|---|---|---|
| 380. Insert Delete GetRandom O(1) | Solution | Solve | Medium | Affirm+41 | ||
| 382. Linked List Random Node | Solution | Solve | Medium | Google+2 | ||
| 384. Shuffle an Array | Solution | Solve | Medium | Amazon+6 | ||
| 398. Random Pick Index | Solution | Solve | Medium | Amazon+2 | ||
| 470. Implement Rand10() Using Rand7() | Solution | Solve | Medium | Bloomberg+6 | ||
| 478. Generate Random Point in a Circle | Solution | Solve | Medium | Leap Motion+1 | ||
| 497. Random Point in Non-overlapping Rectangles | Solution | Solve | Medium | Google+1 | ||
| 519. Random Flip Matrix | Solution | Solve | Medium | Google | ||
| 528. Random Pick with Weight | Solution | Solve | Medium | 6Sense+25 |
Frequently appear alongside Randomized.
Common questions about Randomized.
Randomized algorithms use random numbers or probabilistic decisions during execution. Instead of guaranteeing the same path every run, they produce correct results with high probability or achieve better expected performance. Common examples include randomized quickselect, random shuffling, and reservoir sampling.
Yes, randomized techniques occasionally appear in FAANG interviews, especially in problems involving sampling, load balancing, or avoiding worst-case scenarios. Understanding expected complexity and uniform probability distributions can give you an advantage.
Common patterns include reservoir sampling, random index selection, random shuffling, weighted random selection, and randomized partitioning in selection algorithms. These patterns often combine arrays, hash tables, and probability concepts.
Start with probability basics, then implement simple tasks like random shuffling and uniform selection. After that, practice structured patterns such as reservoir sampling and randomized quickselect while analyzing expected time complexity.
Popular interview problems include random pick from an array, random pick with weight, shuffle an array, and reservoir sampling from a data stream. These problems test your ability to produce uniform distributions and analyze expected time complexity.
Most candidates can build strong intuition by solving around 10–20 well‑selected problems. FleetCode’s 12 randomized practice questions cover the most common interview patterns such as sampling, shuffling, and randomized selection.
