---
id: 67f413a2abe9894c52e15c6e
title: Dynamic Programming Quiz
challengeType: 8
dashedName: quiz-dynamic-programming
---

# --description--

To pass the quiz, you must correctly answer at least 9 of the 10 questions below.

# --quizzes--

## --quiz--

### --question--

#### --text--

What are the two essential properties that must be present in a problem for dynamic programming to be an effective solution approach?

#### --distractors--

Fast execution time and minimal memory usage

---

Recursion capability and iterative loops

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Sequential processing and parallel computation

#### --answer--

Overlapping subproblems and optimal substructure

### --question--

#### --text--

What is the primary difference between the memoization and tabulation approaches in dynamic programming?

#### --distractors--

Memoization uses hash tables while tabulation uses arrays, making it more efficient.

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Memoization is faster but uses more memory and CPU cycles than tabulation.

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Memoization can only solve simpler problems than tabulation.

#### --answer--

Memoization is a top-down approach using recursion, while tabulation is a bottom-up approach using iteration.

### --question--

#### --text--

Why do naive recursive solutions to dynamic programming problems typically have exponential time complexity?

#### --distractors--

Because they use exponential amounts of memory to store variables.

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Because they require sorting data in exponential time.

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Because they must check all possible permutations of the input.

#### --answer--

Because each recursive call branches multiple times, causing the same subproblems to be recalculated repeatedly.

### --question--

#### --text--

What does optimal substructure mean in the context of dynamic programming?

#### --distractors--

The algorithm must use the most efficient data structure available.

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The solution must minimize both time and space complexity simultaneously.

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The problem must have a unique, single optimal solution.

#### --answer--

The optimal solution can be constructed from optimal solutions to its subproblems.

### --question--

#### --text--

When implementing memoization, what happens when a function is called with arguments that have already been computed?

#### --distractors--

The function recalculates the result to ensure accuracy.

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The function averages the old and new results for better precision.

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An error is thrown because duplicate calculations are not allowed.

#### --answer--

The cached result is returned immediately without recomputation.

### --question--

#### --text--

What is a key advantage of using tabulation instead of memoization?

#### --distractors--

Tabulation always requires less memory than memoization.

---

Tabulation can solve a broader class of problems.

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Tabulation is always easier to implement and understand.

#### --answer--

Tabulation avoids recursion overhead and provides predictable sequential execution.

### --question--

#### --text--

In a bottom-up dynamic programming solution, why are base cases initialized first?

#### --distractors--

To allocate memory for the data structure efficiently.

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To prevent infinite loops in the algorithm.

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To improve the time complexity of the algorithm.

#### --answer--

To provide foundational values upon which all larger subproblems are built.

### --question--

#### --text--

How does dynamic programming transform the time complexity of problems that exhibit overlapping subproblems?

#### --distractors--

From polynomial to logarithmic by dividing the problem efficiently.

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From quadratic to linear by optimizing loop structures.

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From linear to constant by using hash tables.

#### --answer--

From exponential to polynomial by storing and reusing subproblem solutions.

### --question--

#### --text--

What trade-off does dynamic programming typically make to achieve better time complexity?

#### --distractors--

It sacrifices code readability for faster execution.

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It requires more complex algorithms that are harder to maintain.

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It limits the size of problems that can be solved.

#### --answer--

It uses additional space to store intermediate results.

### --question--

#### --text--

In which scenario would dynamic programming NOT be the appropriate algorithmic approach?

#### --distractors--

When the problem requires finding an optimal solution.

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When the problem can be broken into smaller subproblems.

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When space complexity must be minimized.

#### --answer--

When subproblems are independent and don't overlap.
