# Implement a k-nearest neighbors algorithm Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/questions/3489/implement-k-nearest-neighbors-algorithm Source observed: 2026-11-04T22:03:14.769Z Last checked: 2026-11-04T22:03:14.769+00:00 Indexed: 2026-11-06T09:01:25.344+00:00 --- # Implement a k-nearest neighbors algorithm **Difficulty:** Easy **Interview categories:** Coding; Machine Learning **Roles:** Machine Learning Engineer; Software Engineer **Companies associated with this question:** Meta, Amazon, Pinterest **Reported activity:** 3 interviews; last asked 4 months ago **Community:** No answers contributed yet ## Problem Implement the K-nearest neighbors (KNN) algorithm. Your `knn` function has four inputs: 1. `X_train`: a NumPy array of points already on the map. 2. `Y_train`: a NumPy array of labels for each point on the map, either `1` or `0`. 3. `X_new`: a NumPy array for the new point being added to the map and classified. 4. `k`: an integer specifying how many of the closest points to `X_new` to inspect when deciding its label. KNN classifies a new data point using the most common category among its `k` closest points in the dataset. It determines closeness by calculating distances between points. ## Python example ```python # Example X_train = np.array([[1, 2], [2, 3], [3, 4], [6, 7], [7, 8], [8, 9]]) y_train = np.array([0, 0, 0, 1, 1, 1]) X_new = np.array([2, 2]) # The new point we want to classify k = 3 # We'll look at the 3 closest points knn(X_train, y_train, X_new, k) # should return 0 ``` The interactive coding area includes **Question**, **Solution**, **Mock Interview**, and **Community** tabs. The editor includes **Editor** and **Share** views, with **Output**, **Test Results**, and **Console** controls. Running code requires signing up: [Sign up to run code](https://www.aced.io/signup?src=lesson). ## Interview preparation Use our [Model & Algorithm Fundamentals](https://www.tryexponent.com/courses/ml-engineer/ml-concepts-interviews/k-nearest-neighbors) module to study KNN for interviews. Detailed company statistics are available through [Upgrade](https://www.aced.io/upgrade?src=questions). ## Interview experience One shared experience is associated with this question: [Meta · 4 months ago](https://www.aced.io/experiences/meta-facebook-senior-machine-learning-engineer-interview-9efbbd). The contributor, a **Machine Learning Engineer at Senior / L5**, described an initial recruiter screen, a 45-minute pre-screen with a senior manager about past work and impact, and a full loop of five interviews. The loop included two coding rounds—one classic LeetCode-style round and one AI-coding round with data-processing and math-heavy prompts such as sparse-matrix-times-vector multiplication and KNN search—along with two design rounds and one behavioral round. The design rounds covered ML modeling for a computer-vision problem and system design resembling Google Street View. The contributor reported passing the loop and moving into offer negotiation. ## Related question - [Implement a string sorting algorithm, treating digits as numbers.](https://www.aced.io/questions/2059/implement-string-sorting-algorithm-treating-digits-as-numbers) ## Related courses ### [Machine Learning Engineer Interview Prep](https://www.aced.io/courses/ml-engineer) ![Machine Learning Engineer Interview Prep](https://images.ctfassets.net/x78yjrjc11pq/6UfkHxUyLQVfDHSfLNQtWa/1be1539a65b4f6f15201f5b573c0a578/ML_Engineer_Interview_Prep_Course.jpeg?w=800&h=800&fm=webp&q=75) Prepare for machine-learning roles at Meta, Google, Amazon, Apple, Microsoft, Nvidia, and other companies with mock interviews, frameworks, and advice from senior candidates. 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