Dot product - ML & AI Coding

244. Dot product

easy

General

senior

Compute the dot product between two vectors, a core linear algebra operation used in ML for measuring similarity and building linear models. In this task, you’ll implement it effectively using NumPy.

\mathbf{x} \cdot \mathbf{y} = \sum_{i=1}^{n} x_i y_i

Requirements

Implement the function:

python

Rules:

Compute the dot product defined by: $x \cdot y = \sum_{i=1}^{n} x_i y_i$
Return a single float value.
You can use any valid NumPy method (e.g., np.dot, @ operator, or sum(x * y)).
Input vectors are NumPy arrays.

Example

python

Output:

python

Input Signature

Argument	Type
x	np.ndarray
y	np.ndarray

Output Signature

Return Name	Type
value	float

Constraints

Inputs are NumPy arrays; output float
Single function; O(n) time

Hint 1

Use np.dot(x, y) for a direct calculation.

Hint 2

Alternatively, x @ y or np.sum(x * y) achieves the same result.

Hint 3

Ensure the inputs are NumPy arrays for these operators to work efficiently.

Roles

ML Engineer

AI Engineer

Data Scientist

Quantitative Analyst

Companies

General

Levels

senior

entry

Input Arguments

Edit values below to test with custom inputs

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