csa2

csa2

12th Grade

25 Qs

quiz-placeholder

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csa2

csa2

Assessment

Quiz

Other

12th Grade

Practice Problem

Easy

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25 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does memoization improve performance?

It replaces loops with recursion

It increases memory usage

It deletes unnecessary variables

It stores the results of expensive function calls and returns the cached result when the same inputs occur again

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a linked list?

A function that stores variables

A data structure where elements are connected via pointers and each node points to the next

A static array

A method for sorting numbers

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a priority queue?

A queue where elements are dequeued based on priority rather than order of insertion

A queue that removes elements in LIFO order

A queue that only stores numbers

A function for iterating lists

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is the difference between an array and a linked list?

Arrays store elements in reverse order

Arrays have fixed sizes and contiguous memory, while linked lists use dynamic memory and nodes that point to each other

Linked lists can only store integers

Arrays and linked lists are identical

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is overfitting in machine learning?

A situation where a model learns training data too well, capturing noise and failing to generalize to new data

A method for training neural networks

A technique for reducing memory usage

A function that always returns the same result

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What is a neural network?

A programming language

A database system

A computational model inspired by biological neurons, consisting of interconnected layers that process data

A sorting algorithm

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

How does backpropagation work in deep learning?

It removes irrelevant training data

It randomly assigns values to neurons

It adjusts model weights by calculating gradients using the chain rule and propagating errors backward

It prevents the model from updating weights

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