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

The number of elements in the tensor

The number of dimensions in the tensor

The highest value in the tensor

The mean value of the tensor

By using the tf.reshape() function

By using the tf.concat() function

By using the tf.assign() function

In-place tensor operations are not supported in TensorFlow

To increase the dimensionality of the tensor

To reduce the dimensionality of the tensor

To change the data type of the tensor

To normalize the values within the tensor

In-place operations are not supported in a multi-threaded environment.

Each thread operates on a separate copy of the tensor.

Concurrent in-place operations can lead to race conditions and unexpected results.

In-place operations are serialized to ensure thread safety.

In-place operations modify the original tensor, while out-of-place operations create a new tensor.

Out-of-place operations are always faster than in-place operations.

In-place operations are only supported in PyTorch, while out-of-place operations are supported in TensorFlow.

Out-of-place operations consume more memory than in-place operations.

Tensor shapes must be predefined and cannot change during computation.

TensorFlow automatically adjusts tensor shapes based on the input data.

TensorFlow requires all tensors within a computation graph to have the same shape.

TensorFlow does not support multi-dimensional tensors.

Using the ":" operator with a step size of 2

Using the "[]" operator with a step size of 2

Using the "%" operator with a step size of 2

Slicing every other element is not possible in Python

All tensor operations in PyTorch are performed in place by default

PyTorch does not support tensor operations

In-place tensor operations are always more memory-efficient than out-of-place operations

PyTorch provides both in-place and out-of-place versions of tensor operations

Transmitting tensor data over a network

Reshaping tensors to have compatible shapes for arithmetic operations

Splitting a tensor into smaller tensors

Combining multiple tensors into a single tensor

Operating on individual elements of tensors independently

Operating on the entire tensor as a single entity

Operating on subsets of elements within a tensor

Operating on elements based on their index position

Reshaping

Transposing

Element-wise addition

Concatenation

Slicing accesses individual elements, while indexing accesses subsets of elements.

Slicing operates on multiple dimensions, while indexing operates on a single dimension.

Slicing creates a new tensor, while indexing modifies the original tensor in place.

Slicing only works on one-dimensional tensors, while indexing works on multi-dimensional tensors.

Changing the order of elements within the tensor

Changing the shape of the tensor

Creating a new tensor with the same elements in a different arrangement

Deleting the tensor from memory

Transposing

Flattening

Concatenating

Element-wise multiplication

It can only be one-dimensional

It is fixed and cannot change during computation

It represents the size of the tensor along each axis

It determines the data type of the tensor

Convolution

Matrix multiplication

Sorting

Feature scaling

The size of the tensor

The number of elements in the tensor

The number of axes or indices required to access its elements

The data type of the tensor

By adding -1 to the index

By subtracting the index from the length of the tensor

By specifying the index as a negative value

Negative indices are not supported in Python

Reshaping

Transposing

Element-wise addition

Concatenation

Reduced memory usage

Increased memory usage

No impact on memory usage

Faster computation time

The shape of a tensor determines its data type

All tensors within a dataset must have the same shape

The shape of a tensor can change during computation

Tensor shape is always one-dimensional

Modifying the tensor directly without creating a new copy

Creating a new tensor with the same values

Deleting the tensor from memory

Halting computation on the tensor

Dividing the tensor into smaller tensors

Removing elements from the tensor

Accessing a subset of elements from the tensor

Applying a mathematical operation to the tensor

The number of elements in the tensor

The number of dimensions in the tensor

The maximum value in the tensor

The mean value of the tensor

Using only row indices

Using only column indices

Using a combination of indices for each dimension

Using a single index for the entire tensor

A scalar value

A multi-dimensional array

A singular value decomposition

A statistical measure of dispersion

Changing the order of elements within the tensor

Changing the number of elements in the tensor

Changing the shape of the tensor without modifying the data

Deleting the tensor from memory

To reduce the number of dimensions in the tensor

To increase the number of dimensions in the tensor

To transpose the tensor

To perform element-wise multiplication

Reshaping

Transposing

Concatenation

Flattening

The size of the tensors

The data type of the tensors

The shape of the tensors

The axis argument provided to the concatenation function