Tensor Basic Concepts in PyTorch QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

What is the result of tensor[::2] in Python slicing?

Every second element starting from the first

Every second element starting from the second

Every element starting from the second

Every element starting from the first

Question: 2

What can be a challenge when using in-place operations in neural network training with backpropagation?

Improved convergence

Vanishing gradients

Overfitting

Increased computational cost

Question: 3

What is a common use case for in-place operations in optimization algorithms?

Feature extraction

Gradient descent updates

Regularization

Model evaluation

Question: 4

What does tensor[-2:-5:-1] represent in Python slicing?

Elements from index -2 to -5 (inclusive)

Elements from index -2 to -5 (exclusive)

Elements from index -5 to -2 (inclusive)

Elements from index -5 to -2 (exclusive)

Question: 5

How do you extract all elements from the third column of a 2D tensor in Python?

tensor[:, 3]

tensor[2, :]

tensor[3, :]

tensor[:, 2]

Question: 6

What is the shape of a tensor resulting from element-wise addition of two tensors with shapes (3, 4) and (3, 4)?

(3, 4)

(6, 8)

(3,)

(4, 3)

Question: 7

If you have a tensor with shape (5, 1), what is its rank?

Question: 8

What is an important consideration when using in-place operations in a multi-threaded environment?

Improved parallelism

Potential race conditions

Increased accuracy

Compatibility with distributed computing

Question: 9

In deep learning frameworks, what is a potential use case for in-place operations?

Increasing the interpretability of models

Enabling real-time data augmentation

Implementing complex mathematical functions

Reducing the need for transfer learning

Question: 10

What is the result of tensor[1:4:2] in Python slicing?

Elements 1 and 3

Elements 1 and 2

Elements 2 and 4

Elements 2 and 3

Question: 11

How do you reverse the order of elements in a one-dimensional tensor in Python?

tensor[::-1]

tensor[0:-1]

tensor[1:]

tensor[-1:0]

Question: 12

What does the term "tensor rank" refer to?

The number of dimensions in the tensor

The maximum element value in the tensor

The size of the tensor

The data type of the tensor

Question: 13

If you reshape a tensor with shape (2, 3, 4) into (6, 4), how many axes does the resulting tensor have?

Question: 14

Disadvantage of frequent in-place operations for code readability?

Lengthy code

Reduced performance

Difficulty tracking variable changes

Hardware compatibility issues

Question: 15

Why prefer in-place operations in deep learning frameworks?

Improved accuracy

Reduced gradient computation

Enhanced efficiency and memory usage

Required for parallel processing

Question: 16

What is the dimensionality of a scalar tensor?

Question: 17

In a 2D tensor, how do you access the element in the second row and third column?

tensor[1, 2]

tensor[2, 3]

tensor[1][2]

tensor[2][3]

Question: 18

What is the shape of a scalar tensor in TensorFlow?

()

(1,)

(1, 1)

(0,)

Question: 19

If you have a tensor with shape (2, 3, 4), how many total elements does it contain?

Question: 20

Which operation is an example of an in-place operation?

tensor + other_tensor

tensor * scalar

tensor += other_tensor

tensor / scalar

Question: 21

What happens when you perform an in-place addition on a tensor?

A new tensor is created with the added values

The original tensor is modified with the added values

The operation raises an error

The tensor becomes immutable

Question: 22

What does tensor[:3] represent in Python slicing?

All elements from index 0 to 3 (exclusive)

All elements from index 1 to 3 (inclusive)

All elements from index 0 to 2 (inclusive)

All elements from index 1 to 2 (exclusive)

Question: 23

How do you access the last element of a one-dimensional tensor in Python?

tensor[-1]

tensor[0]

tensor[len(tensor)]

tensor[-2]

Question: 24

What does the term "rank" refer to in the context of tensors?

The size of the tensor

The data type of the tensor

The number of dimensions in the tensor

The maximum element value in the tensor

Question: 25

How is the shape of a tensor represented in Python?

Tuple

List

Set

Dictionary

Question: 26

What is a potential drawback of frequently using in-place operations?

Slower execution

Increased memory consumption

Limited expressiveness

Difficulty in debugging

Question: 27

What is the primary advantage of in-place operations on tensors?

Improved performance and memory efficiency

Simplicity of syntax

Compatibility with older Python versions

Automatic differentiation

Question: 28

What is the purpose of tensor slicing?

To change the data type of a tensor

To extract a subset of a tensor's elements

To concatenate two tensors

To perform matrix multiplication

Question: 29

In Python, how would you access the third element of a one-dimensional tensor using indexing?

tensor[1]

tensor[2]

tensor[3]