Minimal Operations
Reported by candidates from Palantir's online assessment. Pattern, common pitfall, and the honest play if you blank under the timer.
Palantir's September OA included a problem called 'Minimal Operations' with no obvious pattern hints from candidates. You're facing a problem where you need to find the minimum number of steps to reach a goal state, likely by applying specific allowed operations. These problems hinge on finding the shortest path or the greedy sequence of moves. StealthCoder can read the exact operation set and constraints in real time, then guide you to the right framing if you freeze on the approach.
Pattern and pitfall
Minimal Operations problems typically fall into two camps: BFS-based shortest path (when you need to explore all reachable states) or greedy simulation (when there's an obvious optimal choice at each step). The trick is recognizing which bucket you're in within the first minute. If the problem lets you work backwards from the goal, that's often faster. Common pitfall: candidates implement BFS correctly but blow the memory or time budget on a state space that's too large. The pattern usually involves either breadth-first-search, greedy logic, or occasionally math if there's a closed-form formula. StealthCoder can identify the state space size and suggest pruning strategies before you commit to code.
The honest play: practice the pattern, and have StealthCoder ready for the one you didn't see coming.
You can drill Minimal Operations cold, or you can hedge it. StealthCoder runs invisibly during screen share and surfaces a working solution in under 2 seconds. The proctor sees the IDE. They don't see what's behind it. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play.
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Palantir reuses patterns across OAs. Built for the candidate who saw this exact problem leak two days before his OA and wondered if anyone had a play. Works on HackerRank, CodeSignal, CoderPad, and Karat.
Minimal Operations FAQ
Is this a BFS problem or can I solve it greedily?+
Read the operations carefully. If each operation has a clear 'best' choice at every step, greedy works. If the optimal move depends on future state, you need BFS. Palantir usually expects you to justify why one works over the other.
What if the state space is huge?+
Work backwards from the goal if possible, or identify mathematical invariants that reduce the search. Palantir problems often have a hidden constraint that limits branching. Don't assume brute-force BFS is the answer.
How do I avoid infinite loops in a simulation?+
Use a visited set for BFS. For greedy, prove that your chosen operation always makes progress toward the goal. If stuck, ask yourself: does this operation decrease some metric that must eventually hit zero?
Should I code this in Python or a compiled language?+
Speed rarely matters on Palantir OAs. Write in whatever you're fastest in. A correct Python solution beats a half-finished Java solution every time.
What if I blank on the algorithm in the first 5 minutes?+
Start by writing out 2-3 example traces by hand. Watch the pattern emerge. If you're still stuck after 10 minutes, a straightforward BFS that explores all single-operation transitions is a safe fallback.