Google interview problems are rarely about long descriptions or tricky wording. Instead, they tend to hide key constraints in just a few lines, and those small details determine whether you can find an efficient solution. Recently, a candidate faced two such problems in a Google interview, and with the help of CSOAHelp, he was able to break down the challenges and present confident, optimized answers.

Problem A: Minimum Distance Between People and Cakes

Original problem statement:

We have a 1D space, which could be represented as an int array.
Every element in this array can have one of the three values: {0, 1, 2}.
They have the following meanings:
0 -- space is empty
1 -- there is a person at the space
2 -- there is a cake at the space

The distance between a cake and a person is defined as the number of spaces between them.
Please write a method to get the minimum distance between any person and any cake in the space.

Follow-up conditions:

• Each person will choose the nearest cake.
• If that cake is already taken, the person must choose the next nearest one.
• The array guarantees the number of cakes is greater than or equal to the number of people.
• There are no ties: no two cakes are equidistant from the same person, and no two people are equally close to the same cake.

Where candidates get stuck:
The first instinct is often brute force: for every person, compute the distance to every cake and take the minimum. That works in principle but collapses in efficiency when the array grows large.

How CSOAHelp guided the solution:
We first highlighted the crucial constraints: the array is ordered, the number of cakes is always sufficient, and there are no ties. Together, these mean the problem can be solved by scanning the array once with a two-pointer greedy approach.

Imagine placing people and cakes along the line in sorted order. Each person simply pairs with their closest cake, and because there are no ties, the assignments never overlap. Once a cake is taken, the pointer advances, ensuring that the next person will only consider the remaining cakes. This logic guarantees linear time complexity O(N).

When the interviewer pushed further—“How do you prove there will be no conflicts?”—the candidate confidently answered: the uniqueness of distances ensures each assignment is stable and non-overlapping. With CSOAHelp’s prompts, he transformed what looked like a global matching problem into a straightforward greedy algorithm, complete with a polished complexity explanation.

Problem B: Matrix Path with Monotonic Constraint

Original problem statement:

Given a matrix of positive integers. 
You need to walk from the most top left element to the most bottom right element.
You can only walk to the up, down, left or right neighbor elements.
You can only walk to a neighbouring element that is less than or equal to the current element.

Where candidates get stuck:
This is essentially a reachability problem on a grid. Many candidates immediately attempt a DFS solution, but without careful control, they risk revisiting the same cells repeatedly, leading to inefficiency or even infinite recursion.

How CSOAHelp guided the solution:
We reframed the problem: the constraint that you can only move to a neighbor with value less than or equal to the current one means that the path is monotonically non-increasing. This property eliminates cycles. Once you move down to a smaller or equal value, you cannot loop back up to a higher one.

With this insight, BFS becomes the cleanest approach. Each cell is visited at most once, and the use of a visited set guarantees no redundant work. The time complexity is simply O(M×N), since each of the M×N cells is processed at most once. In addition, BFS naturally finds the shortest path if needed, which was an extra point the interviewer appreciated.

When asked why infinite loops cannot occur, the candidate could confidently explain: monotonicity makes the graph effectively a DAG (directed acyclic graph). This simple, logical answer demonstrated not just coding ability but real problem understanding.

Why CSOAHelp Made the Difference

The beauty—and the difficulty—of Google-style problems is that the hidden constraints change everything.

• In the cake problem, “no ties” and “ordered array” downgraded a global matching task into a linear greedy solution.
• In the matrix path problem, “non-increasing moves” eliminated cycles and made BFS both safe and efficient.

Without recognizing these constraints, a candidate might spend the entire interview stuck on brute force or debugging loops. With CSOAHelp, the candidate immediately focused on the essence of the problem, avoided dead ends, and delivered polished answers under pressure.

If you’re preparing for Google or any other top-tier tech company, don’t fight through these puzzles alone.

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