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Vector and Matrix Operations Essentials Quiz Quiz

Test your understanding of core concepts in vector and matrix operations with this beginner-friendly quiz. Practice questions on addition, multiplication, scalar products, transposes, and basic properties to strengthen your linear algebra skills.

  1. Vector Addition

    What is the result of adding the vectors (2, 5) and (3, 1)?

    1. (6, 7)
    2. (1, 4)
    3. (5, 6)
    4. (2, 5)

    Explanation: The sum of two vectors is found by adding their corresponding components: (2+3, 5+1) = (5, 6). Option (6, 7) incorrectly adds all numbers, (1, 4) subtracts instead of adding, and (2, 5) simply repeats the first vector.

  2. Matrix Dimensions

    What is the size of the resulting matrix when you multiply a 2x3 matrix by a 3x1 matrix?

    1. 2x3
    2. 2x1
    3. 3x1
    4. 3x2

    Explanation: When multiplying an m x n matrix by an n x p matrix, the result is an m x p matrix, so 2x3 and 3x1 yield 2x1. The other options either flip the dimensions or do not match the correct matrix multiplication rule.

  3. Dot Product Calculation

    What is the dot product of the vectors (1, 4) and (2, 3)?

    1. 10
    2. 14
    3. 11
    4. 7

    Explanation: The dot product is (1*2) + (4*3) = 2 + 12 = 14. Other options come from mixing operations, such as adding instead of multiplying (10 or 7), or swapping the multiplication order.

  4. Scalar Multiplication

    If you multiply the vector (2, -1) by the scalar 3, what is the result?

    1. (5, 2)
    2. (6, -3)
    3. (-6, 3)
    4. (3, -2)

    Explanation: Each component is multiplied by 3, giving (2*3, -1*3) = (6, -3). The distractors are formed by incorrect arithmetic: (5,2) adds values, (3,-2) only multiplies one component, and (-6,3) reverses the sign.

  5. Matrix Addition

    Which is the result of adding matrices [[1, 2], [3, 4]] and [[4, 2], [1, 0]]?

    1. [[5, 4], [4, 4]]
    2. [[3, 0], [2, 4]]
    3. [[1, 4], [2, 4]]
    4. [[4, 2], [1, 0]]

    Explanation: Matrix addition happens element-wise: [[1+4, 2+2], [3+1, 4+0]] = [[5, 4], [4, 4]]. The incorrect options either swap elements, present the original matrix, or compute some elements incorrectly.

  6. Transposing a Matrix

    What is the transpose of the matrix [[2, 3], [4, 1]]?

    1. [[4, 2], [1, 3]]
    2. [[2, 4], [3, 1]]
    3. [[2, 3], [4, 1]]
    4. [[3, 2], [4, 1]]

    Explanation: Transposing switches rows and columns, so row (2,3) and (4,1) become columns, resulting in [[2, 4], [3, 1]]. Other answers either do not transpose properly or mix up element order.

  7. Zero Vector

    What is the result of adding any vector to the zero vector in the same space?

    1. The original vector
    2. A vector of ones
    3. The zero vector
    4. A negative vector

    Explanation: Adding the zero vector does not change the original vector, as adding zero to any component leaves it unchanged. The zero vector as a result is incorrect unless adding zeros to zeros. Vectors of ones or negatives are not produced by adding zero vectors.

  8. Matrix Multiplication Rule

    Which of the following statements about matrix multiplication is true?

    1. Both matrices must be square
    2. The result is always a square matrix
    3. The number of columns in the first matrix must equal the number of rows in the second
    4. Only row vectors can be multiplied

    Explanation: For matrices to be multiplied, the columns of the first must match the rows of the second. They do not have to be square, and any compatible matrices work, not just row vectors. The result is not always a square matrix.

  9. Identity Matrix Property

    What happens when a vector is multiplied by the identity matrix of matching size?

    1. The vector remains unchanged
    2. All values are doubled
    3. The vector is transposed
    4. All values become zero

    Explanation: The identity matrix acts like 1 in matrix multiplication, leaving the vector unchanged. It does not zero or double values, nor does it perform a transpose operation.

  10. Element-wise (Hadamard) Product

    What is the element-wise product of vectors (3, 2, 1) and (2, 0, 4)?

    1. (5, 2, 5)
    2. (1, 0, 4)
    3. (3, 2, 1)
    4. (6, 0, 4)

    Explanation: Element-wise multiplication multiplies each corresponding pair: (3*2, 2*0, 1*4) = (6, 0, 4). Other options make mistakes such as adding or copying values.

  11. Column Vector Definition

    Which of the following best describes a column vector?

    1. A matrix with a single row and one column
    2. A matrix with a single column and multiple rows
    3. A square matrix
    4. A matrix with all zero elements

    Explanation: A column vector is defined by being a single column and as many rows as needed. A matrix with one row is a row vector, not a column vector. 'Square' and 'all zero' are unrelated to this definition.

  12. Commutative Property

    Which operation is always commutative for vectors of the same size?

    1. Addition
    2. Matrix multiplication
    3. Subtraction
    4. Multiplying by a matrix

    Explanation: Vector addition is commutative, so order does not matter. Matrix multiplication is generally not commutative, nor is subtraction. Matrix multiplication and scalar multiplication have different rules.

  13. Scalar Product with Zero

    What will be the result when any vector is multiplied by the scalar zero?

    1. The negative of the original vector
    2. The original vector
    3. The zero vector
    4. A vector of ones

    Explanation: Multiplying by zero results in the zero vector, since all values become zero. The original vector, ones, or negatives are not produced by multiplying with zero.

  14. Matrix Subtraction

    What is [[5, 3], [0, 2]] minus [[2, 1], [0, 1]]?

    1. [[5, 2], [0, 1]]
    2. [[7, 4], [0, 3]]
    3. [[2, 1], [0, 2]]
    4. [[3, 2], [0, 1]]

    Explanation: Subtracting element-wise: [[5-2, 3-1], [0-0, 2-1]] yields [[3, 2], [0, 1]]. Other options either add or incorrectly subtract some components.

  15. Row Vector Identification

    Which matrix is a row vector?

    1. [[1, 2], [3, 4]]
    2. [7, 2, -1]
    3. [[1], [2], [3]]
    4. [[0], [0], [0]]

    Explanation: A row vector has a single row and multiple columns. [[1], [2], [3]] is a column vector, [[1,2],[3,4]] is a 2x2 matrix, and [[0],[0],[0]] is also a column vector.

  16. Multiplying by Negative Scalar

    What happens when you multiply the vector (2, 4, -1) by -1?

    1. All values become positive
    2. Original vector becomes duplicated
    3. Each component changes sign
    4. All values become zero

    Explanation: Multiplying by -1 negates each component: (2, 4, -1) becomes (-2, -4, 1). The other options either wrongly indicate zeroing, duplicating, or incorrectly making all entries positive.

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