This is a very typical Optiver OA: It does not require hard work on algorithms, but tests your systematic modeling ability + state consistency maintenance + extreme sensitivity to boundary conditions. The question itself is not difficult, but if there is no clear data structure design, it is easy to crash due to confusion in the middle and later stages. I am here to accompany the students through the complete implementation process of this question. Here is a complete review from multiple dimensions: question meaning → modeling → key pitfalls → problem-solving framework.
Interview Overview (Optiver OA)
- Company/Position：Optiver｜Software Engineer / Trading Tech related positions
- Assessment form：Online Assessment (Programming + System Simulation)
- Number of questions: 1 comprehensive simulation question
- Question type：Simulation/State machine/Rule-driven modeling
- Language support: Python/Java/C++ and other mainstream languages
- Core test points:
- Accurate modeling of complex business rules
- Long-term consistency maintenance of multi-object state
- Time series + continuous status updates
- Boundary condition (threshold, time, floating point precision) processing capabilities
- Difficulty rating:
- Algorithm difficulty: medium to low
- Logical complexity: medium to high
- Comprehensive pressure: High (very easy to make mistakes due to omission of details)
- Typical elimination point:
- Wrong understanding of wind speed superposition formula
> 15And>= 15Judge and write the opposite- The 300 second continuous safe time for unstable → stable is not properly maintained
- The status is not updated synchronously when inspecting
- Overall style summary:
Non-LeetCode type questions are more like the implementation of a rule engine in a real system. Optiver very much prefers this type of question that "seems simple but is actually easy to write wrong."
Optiver OA real test questions review
Problem-solving ideas
This question is essentially a time-driven system simulation question. The focus is not on the complexity of the algorithm, but on whether the state consistency of multiple balloons can be continuously and accurately maintained in a series of operations. As long as the model is matched correctly, the subsequent implementation is actually a smooth process.
First, we need to clarify the two types of core information maintained in the system: balloon status and environmental status.
The balloon status includes: the team it belongs to, whether it is flying, the current altitude, whether it is stable, and time information related to stability (such as when it started to reach safe wind speed). The environmental status is mainly the wind speed settings at different heights, and the global timestamp (the question guarantees that the time is strictly incremental).
There is an important principle when modeling: wind speed is the global rule and the balloon is a passive object. When setting the wind speed, only record the wind speed layer itself, do not update all balloons at once; only when the balloon is operated or inspected, the real-time wind speed is calculated based on the current time and altitude and the stability is updated. This makes the logic clearer and less error-prone.
Stability judgment is the key to this question. The balloon is stable by default when it takes off, but if the wind speed at the current altitude is strictly greater than 15 m/s, it needs to become unstable immediately. If an unstable balloon wants to regain stability, it must stay at an altitude with safe wind speed (≤15 m/s) for at least 300 seconds. So, you cannot just use a boolean to represent stability. You must additionally record the "starting point of the safe time" to determine whether the condition of 300 consecutive seconds is met.
Specific operations:
- Ascend: Update the altitude and set it to flight status. It defaults to stable, and then immediately calculates the wind speed to determine whether it needs to become unstable.
- Descend: This is a complete reset operation, and the flight status, altitude, stability and time information need to be cleared.
- Wind: Only the wind speed data structure is updated, and the balloon status is not touched.
- Inspect: Before checking, first update the stability status of all flying balloons synchronously, then find out the highest altitude of the stable balloons not in the team, and then return to the stable balloons in the team whose altitude is not lower than this value.
In summary, what this question really tests is: in a system with complex rules and continuously changing states, whether the logic can be maintained rigorously without omitting boundary conditions and it is quite consistent with Optiver's consistent question-setting style.
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