feat(curriculum): add dynamic programming quiz to js v9 cert (#65995)

This commit is contained in:
Kolade Chris
2026-03-01 18:39:40 +01:00
committed by GitHub
parent 95e293d23a
commit 787922295f
4 changed files with 252 additions and 1 deletions
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@@ -3329,6 +3329,12 @@
"In this lab, you will implement a function that converts an adjacency list representation of a graph into an adjacency matrix representation." "In this lab, you will implement a function that converts an adjacency list representation of a graph into an adjacency matrix representation."
] ]
}, },
"quiz-dynamic-programming-js": {
"title": "Dynamic Programming Quiz",
"intro": [
"Test what you've learned about dynamic programming in JavaScript with this quiz."
]
},
"lecture-understanding-functional-programming": { "lecture-understanding-functional-programming": {
"title": "Understanding Functional Programming", "title": "Understanding Functional Programming",
"intro": [ "intro": [
@@ -0,0 +1,234 @@
---
id: 699a068cfe9bb7bccf2b7ec0
title: Dynamic Programming Quiz
challengeType: 8
dashedName: quiz-dynamic-programming-js
---
# --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
---
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.
---
Memoization is faster but uses more memory and CPU cycles than tabulation.
---
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.
---
Because they require sorting data in exponential time.
---
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.
---
The solution must minimize both time and space complexity simultaneously.
---
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.
---
The function averages the old and new results for better precision.
---
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.
---
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.
---
To prevent infinite loops in the algorithm.
---
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.
---
From quadratic to linear by optimizing loop structures.
---
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.
---
It requires more complex algorithms that are harder to maintain.
---
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.
---
When the problem can be broken into smaller subproblems.
---
When space complexity must be minimized.
#### --answer--
When subproblems are independent and don't overlap.
@@ -0,0 +1,11 @@
{
"name": "Dynamic Programming Quiz",
"isUpcomingChange": true,
"blockLabel": "quiz",
"blockLayout": "link",
"dashedName": "quiz-dynamic-programming-js",
"helpCategory": "JavaScript",
"challengeOrder": [
{ "id": "699a068cfe9bb7bccf2b7ec0", "title": "Dynamic Programming Quiz" }
]
}
@@ -318,7 +318,7 @@
{ {
"dashedName": "dynamic-programming", "dashedName": "dynamic-programming",
"comingSoon": true, "comingSoon": true,
"blocks": [] "blocks": ["quiz-dynamic-programming-js"]
}, },
{ {
"dashedName": "functional-programming", "dashedName": "functional-programming",