The Coding & Algorithm Interviews
Coding: You should know at least one programming language really well, preferably C++, Java, Python, Go,
or C. You will be expected to know APIs, Object Orientated Design and Programming, how to test your code,
as well as come up with corner cases and edge cases for code. Note that we focus on conceptual
understanding rather than memorization.
Algorithms: Approach the problem with both bottom-up and top-down algorithms. You will be expected to
know the complexity of an algorithm and how you can improve/change it. Algorithms that are used to solve
Google problems include sorting (plus searching and binary search), divide-and-conquer, dynamic
programming/memoization, greediness, recursion or algorithms linked to a specific data structure. Know
Big-O notations (e.g. run time) and be ready to discuss complex algorithms like Dijkstra and A*. We
recommend discussing or outlining the algorithm you have in mind before writing code.
Sorting: Be familiar with common sorting functions and on what kind of input data they’re efficient on or
not. Think about efficiency means in terms of runtime and space used. For example, in exceptional cases
insertion-sort or radix-sort are much better than the generic QuickSort/MergeSort/HeapSort answers.
Data Structures: You should study up on as many data structures as possible. Data structures most
frequently used are arrays, linked lists, stacks, queues, hash-sets, hash-maps, hash-tables, dictionary, trees
and binary trees, heaps and graphs. You should know the data structure inside out, and what algorithms
tend to go along with each data structure.
Mathematics: Some interviewers ask basic discrete math questions. This is more prevalent at Google than
at other companies because counting problems, probability problems and other Discrete Math 101
situations surround us. Spend some time before the interview refreshing your memory on (or teaching
yourself) the essentials of elementary probability theory and combinatorics. You should be familiar with
n-choose-k problems and their ilk.
Graphs: Consider if a problem can be applied with graph algorithms like distance, search, connectivity,
cycle-detection, etc. There are three basic ways to represent a graph in memory (objects and pointers,
matrix, and adjacency list) — familiarize yourself with each representation and its pros and cons. You should
know the basic graph traversal algorithms, breadth-first search and depth-first search. Know their
computational complexity, their tradeoffs and how to implement them in real code.
Recursion: Many coding problems involve thinking recursively and potentially coding a recursive solution.
Use recursion to find more elegant solutions to problems that can be solved iteratively.
google.com/careers