Prepare for OKX technical interviews with 3 tracked LeetCode questions, sortable by difficulty and topic.
| Title | Difficulty | Topics | Frequency | LeetCode |
|---|---|---|---|---|
| Count Subarrays With Fixed Bounds | Hard | 100% | Solve | |
| Kth Smallest Element in a Sorted Matrix | Medium | 89.1% | Solve | |
| Number of Islands | Medium | 67.7% | Solve |
3 questions total
Company analytics
Questions tracked
3
Questions currently linked to OKX
Topics represented
11
Distinct topics visible in this company set
Dominant difficulty
medium
67% of tracked questions
Avg frequency score
85.6
Mean frequency across this company question set
OKX currently has 3 tracked questions linked to 11 topics, so this page is a good benchmark for how deep the dataset goes on one employer.
The most common topics for OKX include Array, Matrix, Binary Search, and Breadth-First Search. Array alone appears 3 times in the visible topic distribution. That topic spread helps you identify whether this company is repeating a few patterns or testing across a broader surface area.
The current question set averages a frequency score of 85.6, and medium is the dominant difficulty bucket at 67% of tracked coverage. That makes it easier to decide whether to practice for repetition, complexity, or both.
OKX related paths
This section answers the most practical questions about using the OKX route as a company-specific LeetCode study page.
Start with the highest-frequency questions inside Array, Matrix, and Binary Search, then sort the table by frequency to build a shortlist. That gives you a faster first pass through OKX's interview patterns than trying to cover every linked problem at once.
medium makes up 67% of the tracked OKX question set. Use that split to decide whether your study plan should emphasize coverage, realistic interview pressure, or deeper problem solving.
Use this page to identify OKX's strongest topic signal, then open Array and the global most-asked or questions explorer routes to see whether that company-specific pattern is also common across the wider dataset.
