Algorithms · 30 Must-Answer Questions
Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do they still test algorithms in the AI era
THE QUESTION THIS PAGE ANSWERS
ANSWER FIRSTWhat is the key idea behind “Algorithms · 30 Must-Answer Questions”?
Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do they still test algorithms in the AI era
Make the claim earn its place. Use this page as a decision aid, not a definition to memorize. Connect the idea to one real task, one observable result, and one failure that would change your mind.
Write one question you could answer with evidence after trying this idea.
A conclusion that sounds complete but leaves the key assumption untested.
The algorithmic cost curve in “Algorithms · 30 Must-Answer Questions”
“Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do…” is not asking you to memorize steps. It trains you to spot repeated work: as the input grows, how many comparisons, moves, or recursive calls does the program perform?
Find repeated work before declaring something fast
Break “Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do…” into three questions: how input size changes, what each round does, and whether the next round can shrink its search space. Big-O describes growth, not an exact time on every machine; constants, memory, and data distribution still matter.
Theoretical optimum is not always practical optimum
When AI writes an algorithm, trace a small input by hand and benchmark progressively larger inputs. That turns “Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do…” from a slogan into a performance claim you can check.
Take the example one step further
The page first makes this point: “Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do…”. Turn it into a small exercise rather than a sentence to memorize: write down the input, expected result, and the observation that would make you re-check the judgment.
Carry the judgment into the next situation
For a real task, find the repeated work first, ask how input size changes, and use a small benchmark to verify the theoretical judgment. Complexity should not become a label detached from the situation.
- “Algorithms · 30 Must-Answer Questions”: Each question comes with What they're assessing, Answer framework, and Bonus point: Big-O intuition / binary-search premises / recursion risks / BFS vs DFS / sampling strategy / do…
Finish with a small, reversible exercise: put the page's judgment into a real input, write the expected result, and name the signal that would make you stop and verify it.
I turned one judgment from this article into a small experiment I could run today. Knowing what to observe next is more useful than simply remembering the conclusion.
After reading this, I first looked for the conditions behind the idea instead of copying the method into a project. That order made the later trade-offs much clearer.
When this judgment reaches real work, which constraint should be added first? I am curious which step matters most between reading and the first practical attempt.
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