How do you recognize a dynamic programming problem in an interview?

DP problems share two properties: optimal substructure (the best solution to the whole problem relies on best solutions to subproblems) and overlapping subproblems (the same subproblem is solved more than once in a naive recursive approach). If you can write a recursive solution that has exponential time complexity due to repeated work, you almost certainly have a DP problem. Common signals: the problem asks for a minimum, maximum, count, or whether something is possible across a sequence, grid, or set of choices.

What are the most common dynamic programming patterns asked in coding interviews?

The six patterns that cover the vast majority of DP interview questions: 0/1 Knapsack (include or exclude each item), Unbounded Knapsack (items can be reused), Longest Common Subsequence (2D string comparison DP), Fibonacci-style linear DP (each state depends on the previous one or two), Interval DP (solve subproblems on contiguous subarrays), and Tree DP (combine answers from child nodes bottom-up). Most questions in interviews are variations of one of these six.

Do FAANG companies actually ask dynamic programming questions?

Yes. Based on real interview reports in the LeakCode database, DP questions appear across all FAANG companies. Google and Meta ask DP more frequently than Amazon. The category shows up in phone screens at roughly 15% of reported Google rounds and in 20-25% of onsite technical rounds at Google and Meta. Amazon tends toward medium-difficulty DP or avoids it in favor of graphs and trees. Microsoft and Apple both ask DP at a moderate rate.

What is the difference between memoization and tabulation?

Memoization (top-down DP) starts from the original problem and recurses into subproblems, caching results in a hash map or array so each subproblem is only solved once. Tabulation (bottom-up DP) starts from the smallest base cases and iteratively fills a table up to the answer. Both have equivalent asymptotic complexity. Memoization is usually easier to derive from a recursive solution. Tabulation avoids recursion stack overhead and is often easier to optimize for space (rolling array trick).

How should I use LeakCode to prepare for DP interviews?

LeakCode aggregates real candidate-reported interview questions from 7 sources including 1Point3Acres, Blind, and LeetCode Discuss. Search for a target company on LeakCode and filter by topic to see which DP patterns that company has historically asked. This lets you prioritize your prep based on real frequency data rather than guessing.

Dynamic Programming Interview Questions: Complete Guide (2026)

What FAANG companies actually ask, which DP patterns appear most, and how to use real interview data from LeakCode to focus your prep.

What is dynamic programming and why does it dominate interviews?

Dynamic programming is an algorithmic technique that solves complex problems by breaking them into overlapping subproblems and storing the results of those subproblems to avoid redundant computation. It is not a single algorithm but a design pattern that applies to a broad class of optimization and counting problems.

Interviewers at companies like Google, Meta, and Microsoft favor dynamic programming problems because they test multiple skills simultaneously: recursive thinking, state definition, space-time tradeoffs, and code clarity under pressure. A candidate who can correctly identify a DP problem, define the right state, write the recurrence, and then optimize space demonstrates genuine algorithmic fluency.

According to real interview reports in the LeakCode database, dynamic programming questions appear in roughly 18% of all coding rounds across the top 50 companies. At Google and Meta, that figure rises to over 20% of technical rounds. LeakCode has indexed over 51,000 real interview questions, and the DP category is consistently one of the top five most common topics.

The six core dynamic programming patterns

Most dynamic programming interview questions reduce to one of six patterns. Recognizing which pattern applies is the hardest part of solving a DP problem in an interview. Here are the six patterns with what makes each recognizable:

0/1 Knapsack

You have a set of items with weights and values. Each item can be used at most once. Maximize value subject to a weight limit. Recognizable by: binary choice (include or exclude), a capacity constraint, and the word "subset." Common variants: subset sum, partition equal subset sum, target sum.

Unbounded Knapsack

Like 0/1 Knapsack but items can be reused any number of times. Recognizable by: "minimum number of coins," "number of ways to make change," "word break with unlimited reuse." The state does not shrink the item list, only the capacity.

Longest Common Subsequence (LCS) family

2D DP over two strings or sequences. The state is dp[i][j] representing some property of the first i characters of string A and the first j characters of string B. Recognizable by: two strings as input, "edit distance," "longest common," "minimum insertions/deletions," "regular expression matching."

Fibonacci-style linear DP

Each state depends only on the previous one or two states. The simplest DP pattern. Recognizable by: "number of ways to climb stairs," "house robber," "jump game," "decode ways." Often the first DP pattern taught because the recurrence is easy to spot.

Interval DP

DP over all contiguous subarrays. The state is dp[i][j] representing some property of the subarray from index i to j. Solved by iterating over subarray length. Recognizable by: "burst balloons," "minimum cost to cut a stick," "palindrome partitioning," "matrix chain multiplication."

Tree DP

DP computed bottom-up on a tree. The answer at each node depends on answers from its children. Recognizable by: "maximum sum path in a binary tree," "house robber on a tree," "diameter of a tree," any problem on a graph with no cycles.

What dynamic programming question categories appear most in interviews

Based on candidate-reported data across 51,000+ questions in the LeakCode database, here are the DP categories that appear most often in technical interviews at top companies. Note that LeakCode surfaces question categories and patterns, not fabricated specific question names:

1.String DP: problems involving two strings, edit operations, pattern matching, or subsequence relationships. Heavily represented at Google and Meta.
2.Knapsack variants: subset sum, partition problems, coin change. Appears across all companies, especially Amazon and Microsoft.
3.Path counting on grids: how many paths from top-left to bottom-right, minimum cost paths, paths with obstacles. Popular at Google phone screens.
4.Sequence optimization: longest increasing subsequence, maximum subarray extensions, stock trading problems. Common at Meta and Goldman Sachs.
5.Game theory DP: optimal play problems, stone game variants, minimax. Appears in senior-level Google and Meta rounds.

How to approach a DP problem in an interview

When you see a new DP problem in an interview, follow this sequence:

1.Identify the pattern. Ask yourself: is this a knapsack (binary choice with a constraint), a string comparison (two inputs), a sequence problem (one array), or an interval problem (contiguous subarray)? Naming the pattern out loud signals competence to your interviewer.
2.Define the state. What does dp[i] or dp[i][j] represent in plain English? Write this out in a comment before any code. A clearly defined state makes the recurrence obvious.
3.Write the recurrence. How does dp[i] relate to dp[i-1] or dp[i-1][j-1]? Work through one or two examples by hand to verify your recurrence before coding.
4.Handle base cases. What is the value of dp at the boundary? Off-by-one errors in DP almost always come from incorrectly initialized base cases.
5.Optimize if asked. Many tabulation solutions can reduce space from O(n*m) to O(n) or O(1) using a rolling array. Mention this even if the interviewer does not ask.

Search Dynamic Programming on LeakCode

LeakCode has 51,000+ real interview questions from 2,000+ companies. Filter by topic, company, and round type to see exactly which DP patterns your target company asks most.

Browse DP Questions

How LeakCode helps with DP prep

LeakCode aggregates real interview questions reported by actual candidates from seven sources: 1Point3Acres (Chinese-language FAANG forum), Blind, LeetCode Discuss, Glassdoor, Reddit, GeeksforGeeks, and InterviewDB. Every question is classified by topic, round type, and seniority level.

For dynamic programming prep specifically, LeakCode lets you: filter by company to see which DP categories that company asks most, filter by round type to know whether DP appears in phone screens or only onsites, and see recency data to confirm patterns from 2025 and 2026 reports, not just historical guesses.

See also: how LeakCode works, our data sources, and FAQ. Related guides: graph interview questions and two pointer interview questions.