DSA topic · Stage 5

Backtracking interview preparation

Explore a decision tree while restoring state after each choice.

DSA prep Practice 3 questions

Concept

Backtracking fits enumeration under constraints when partial choices can be pruned.

Complexity lens

Describe branching factor, depth, output size, and the cost of copying versus mutation.

Common mistakes

Not undoing a choice
Appending the same mutable result
Missing pruning opportunities

Recognition

Decide from constraints, not labels.

These clues narrow the model. They do not replace an invariant or complexity check.

Recognition clues

The task asks to enumerate valid choices
A partial assignment can be rejected early
Choice order or duplicate inputs affect whether outputs repeat

Implementation options

Choose/explore/unchoose with one mutable path
Index-based recursion
Constraint sets or sorted duplicate skipping for pruning

Interview behavior

Name the decision at each depth
Separate validity, completion, and restoration
Describe complexity with branching, depth, and output size

Patterns that operate on this topic

BacktrackingAt depth d, the path contains exactly d committed choices satisfying every constraint checked so far.

Representative problems

3 current Backtracking questions.

Exact active distribution: 0 easy, 3 medium, 0 hard. External records contain metadata and original guidance only.

External catalogUnverified
Subsets
Make the include-or-skip decision tree explicit.
Backtracking Arrays Backtracking
MediumFree / publicSolve problem
External catalogUnverified
Combination Sum
Control reuse and avoid generating equivalent orders.
Backtracking Arrays Backtracking
MediumFree / publicSolve problem
External catalogUnverified
Permutations
Tests state tracking when order matters.
Backtracking Backtracking
MediumFree / publicSolve problem

Review state

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Question pages let you record local completion. Account status, confidence, notes, and a topic roll-up are shown only when private account persistence is available.

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Review prompts

Was state restored on every path?
Did I copy results at completion?
Could an earlier constraint prune more work?

Prerequisites and next step

Dynamic Programming

Define reusable subproblems before optimizing state transitions and storage.

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Backtracking interview preparation

Decide from constraints, not labels.

Recognition clues

Implementation options

Interview behavior

Patterns that operate on this topic

3 current Backtracking questions.

Subsets

Combination Sum

Permutations

Sign in for private topic progress

Dynamic Programming

Backtracking interview preparation

Decide from constraints, not labels.

Recognition clues

Implementation options

Interview behavior

Patterns that operate on this topic

3 current Backtracking questions.

Subsets

Combination Sum

Permutations

Sign in for private topic progress

Dynamic Programming