Recently, many students are asking OpenAI What is the difficulty of OA? I will share it here. Generally speaking, OpenAI's OA is usually two coding questions, the duration is relatively reasonable, and the difficulty is basically around medium. Below we have compiled some high-frequency real questions for your reference.
Exclusive compilation of OpenAI OA high-frequency real questions
Maximum intersecting interval group (11.6 openai OA)
Finds the largest set of intervals in which a specific interval intersects all other intervals in the set.
Idea: Convert the problem to each interval and calculate the number of intervals that can intersect with it. The key is to observe the necessary and sufficient conditions for the disjoint of two intervals, such as the right endpoint of one interval being smaller than the left endpoint of the other. Efficient calculation uses sorting and binary search to quickly eliminate intervals that are unlikely to intersect. Algorithm selection: sort the intervals by the left and right endpoints respectively, and use binary search to determine the number of disjoint intervals. Complexity analysis Sorting complexity O(n log n), each query O(log n), overall O(n log n).
Minimum number of moves (11.6 openai OA)
Distribute the balls numbered 1~n into three groups A, B, and C. Through the movement operation, the final three groups are spliced To form a strictly increasing sequence 1, 2, 3,..., n. Find the minimum number of moves.
Idea: The minimum number of moves in the problem transformation is equivalent to maximizing the number of correctly placed balls. The key observation is that the correct sequence requires that group A contains 1~p, group B contains p+1~q, and group C contains q+1~n. The efficient solution enumerates the dividing points p and q, and uses the prefix and suffix sum to quickly calculate the number of correctly placed balls in each group. The mathematical expression is the minimum number of moves = n – max (the sum of the correct number of balls in each group). Algorithm complexity After preprocessing, all dividing point enumeration can be completed in O(n) time.
Marble Group Reordering (2.21 openai oa)
There are n marbles numbered 1 to n, initially distributed in three groups A, B, and C. Each operation can move a marble from the current group to any other group. The goal is: sort the three groups separately and then concatenate them in the order of A + B + C. The final result is exactly in ascending order from 1 to n. Ask how many moves are needed at least. The three groups can be empty at the end, and there is no size limit.
Idea: The essence of this question is to enumerate two dividing points and divide 1 to n into three continuous intervals, corresponding to the three groups A, B, and C respectively. For each division method, count the number of marbles currently in the "correct interval corresponding group". These can be kept, and the rest need to be moved. The answer is n minus the maximum number that can be retained. The core is not to simulate movement, but to maximize the number of elements retained.
Wind and Falling Leaves Simulation (2.21 openai oa)
Given a height × width grid, each grid has a number of leaves. Given another wind direction string, each character represents one wind (U/D/L/R). Each time the wind blows, all the leaves will move one block in the specified direction, and the leaves that move out of the boundary will disappear. Ask how many leaves will be left after all the wind blows.
Idea: simulate one by one according to the wind direction string. Every time there is a wind, a new empty grid is created, and the entire leaves of the current grid are translated one grid in the corresponding direction, and those that cross the boundary are directly discarded. Repeat until all winds have been processed, and finally count the total number of leaves remaining in the grid. When implementing, pay attention to the direction and boundary judgment to avoid writing errors.
Sprint OpenAI OA
In summary, OpenAI's OA is not tricky, but it is definitely not giving away points. What really makes the difference is detail processing and coding stability. Time allocation, boundary conditions, and complexity control must be practiced in advance, otherwise it will be easy to lose points on questions that you already know how to do. If you are preparing for OpenAI or other major manufacturers' OA, we have long-term collation of the latest OA real question directions of major manufacturers, and also have actual combat rhythm simulation and targetedOA assistance . The more prepared you are, the more stable you will be on the spot. Opportunities are never about luck, but about preparation.
