Machine Learning Engineer Interview Questions

דניאל ר. - "Implemented a recursive function which returns the length of the list so far. when the returned value equals k + 1 , assign current.next = current.next.next. If I made it back to the head return root.next as the new head of the linked list."See full answer

Khushbu R. - "General Approach (using Max-Heap) Use a max-heap (priority queue) of size k. For each point: Compute the distance to P. Push it into the heap. If heap size > k, remove the farthest point. The heap will contain the k closest points to P. import java.util.*; public class KClosestPoints { static class Point { int x, y; public Point(int x, int y) { this.x = x; this.y = y; } // Euclidean distance squared (no need to take square root) p"See full answer

Laksitha R. - "public static int maxProfitGreedy(int[] stockPrices) { int maxProfit = 0; for(int i = 1; i todayPrice) { maxProfit += tomorrowPrice - todayPrice; } } return maxProfit; } "See full answer

Scott S. - " Functional Requirements Content Ingestion\: Ingest news articles from various sources (websites, social media, etc.). Handle different types of content (text, images, videos). Content Analysis\: Extract and preprocess text from articles. Analyze the content for potential indicators of fake news. Model Training and Prediction\: Use machine learning models to classify content as fake or real. Continuously improve models with new data and f"See full answer

Sachin R. - "If 0's aren't a concern, couldn't we just multiply all numbers. and then divide product by each number in the list ? if there's more than one zero, then we just return an array of 0s if there's one zero, then we just replace 0 with product and rest 0s. what am i missing?"See full answer

Anonymous Aardvark - "in simple words, linear regression helps in predicting the value whereas logistics regression helps in predicting the binary classification. But lets talk through some example Linear regression model: E-commerce website pricing recommendation engine is built on linear regression model where we do have some variables such as competitor price, internal economics and consumer demand etc when we put this in a supervised learning model, it helps in predicting prices Logistics regression model"See full answer

Victor H. - "There is a faster approach that solves the problem in O(n) time: def find_duplicates(arr1, arr2): arr1 = set(arr1) res = [] for num in arr2: if num in arr1: res.append(num) return res `"See full answer

Dinesh K. - "i dont know"See full answer

Will I. - "Is it bad to get the answer a different way? Will they mark that as not knowing Bayes Theorem or just correct as it is an easier way to get the answer? The way I went is to look at what happens when the factory makes 100 light bulbs. Machine A makes 60 of which 3 are faulty, Machine B makes 40 of which 1.2 are faulty. Therefore the pool of faulty lightbulbs is 3/4.2 = 5/7 from machine A and 1.2/4.2 = 3/7 from Machine B."See full answer

Shuchi A. - "A few months ago I joined a micro-services platform engineering team as their manager, at that time my team was struggling to deliver towards an upcoming production deadline for a customer facing product. Production date had been moved 5 times already and there were about 40% of product features which were remaining to be tested and signed off to move to production . I was made responsible to deliver the release of this product within the deadline and turnaround the software delivery throughput."See full answer

Nidhi S. - "There are couple of reasons for it - Kind of role : Its a product manager role loaded with analytical work, So working with data in stringent regulatory guideline make it more exciting and thrilling. Location & industry is like - Cherry on the cake, Bangalore weather and BFI is at its all time peak as people spending behavior is changing continuously, it will be interesting to see big giants like visa are managing it."See full answer

Bless M. - "You can ask some clarifying questions like 1) Ask if the list is already sorted or not 2) is zero included in the list ? 3) Natural numbers are usually positive numbers ( clarify they are non negatives) Solution : 1) If sorted use two pointers and sort them in O(N) 2) if not sorted , -ve / only +ve numbers in the list doesn't matter - the easiest solution is Use a priority queue and push the number and its square in each iteration Finally return the list returned by the priority Queue. N"See full answer

Dinar M. - "Even more faster and vectorized version, using np.linalg.norm - to avoid loop and np.argpartition to select lowest k. We dont need to sort whole array - we need to be sure that first k elements are lower than the rest. import numpy as np def knn(Xtrain, ytrain, X_new, k): distances = np.linalg.norm(Xtrain - Xnew, axis=1) k_indices = np.argpartition(distances, k)[:k] # O(N) selection instead of O(N log N) sort return int(np.sum(ytrain[kindices]) > k / 2.0) `"See full answer

Anonymous Hare - "Sharing the approach for functional requirements we tool to solve this question. Functional Requirements This is only for the Registered users What is a "For You" page ? Home page where you get suggestions based on people you follow. Interactions like/share/comments (done by user) Interests (shared by the user during registration or onboarding) sports choices/ region choices/ Video sharing platform. So how many videos should we s"See full answer