Amazon Machine Learning Engineer Interview Questions

Taheia S. - "at first I want to know number of cluster I will put random number if I don't know and I will use method called Elbow method or Silhouette Score ,Gap Statistic and Davies–Bouldin Index to know the best number of cluster and I will use scikit-learn library to import kmeans from sklearn.cluster import KMeans kmeans = KMeans(nclusters=2, randomstate=0) kmeans.fit(X) and X this my data "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

Jeff S. - " class Node { constructor(data) { this.data = data; this.left = null; this.right = null; } } function diameterOfTree(root) { if (root === null || root.left === null & root.right === null) { return 0; } function countBranch(node, count) { if (node.left === null && node.right === null) { return count; } let left = node.left === null ? 0 : countBranch(node.left, count+1); let right = no"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

Maciej Z. - "function twoSum(nums, target) { const n = nums.length const map = new Map() for (let i=0; i<n; i++) { if (map.has(nums[i])) return [map.get(nums[i]), i] const diff = target - nums[i] map.set(diff, i) } return [] } `"See full answer

onering2ruleall - "My leadership style is flexible and adaptive, it varies depending on the team members and the needs of the company. My leadership goal is to empower the team and inspire and grow leaders. In order to achieve that, I combine transformational, democratic and coaching leadership styles. Usually when we are facing a new type of challenge, or at the early stage of a project, I like to adapt the transformational leadership which allows me to listen to all the suggestions from the team members and sta"See full answer

Rick E. - " from typing import List def getnumberof_islands(binaryMatrix: List[List[int]]) -> int: if not binaryMatrix: return 0 rows = len(binaryMatrix) cols = len(binaryMatrix[0]) islands = 0 for r in range(rows): for c in range(cols): if binaryMatrixr == 1: islands += 1 dfs(binaryMatrix, r, c) return islands def dfs(grid, r, c): if ( r = len(grid) "See full answer

Aldar M. - "/my initial thought was to make an example with program integration project that I led a few years ago Stage 1: Project Planning. The first stage of SDLC is all about “What do we want?” my approach was simply take project life cycle structure(initiation, planning, execution, monitoring and controlling, and closure) and elaborate on each stages did i. I lack a details and Excell skills and I should have taken SDLC structure because I fits better with JD. Stage 2: Gathering Requirements & Ana"See full answer

Tiago R. - "function addChildren(root, val, inorder) { const rootInOrderIndex = inorder.indexOf(root.value); const childrenInOrderIndex = inorder.indexOf(val); if (childrenInOrderIndex < rootInOrderIndex) { if (!root.left) { root.left = new TreeNode(val); } else { addChildren(root.left, val, inorder); } } else { if (!root.right) { root.right = new TreeNode(val); } else { addChildren(root.right,"See full answer

Manoj R. - "def traprainwater(height: List[int]) -> int: n = len(height) totalwaterlevel = 0 for i in range(n): j = i+1 while j = n: break rows = j - i -1 intrwaterlevel = min(height[j], height[i]) * rows for k in range(i+1, j): intrwaterlevel -= height[k] totalwaterlevel += intrwaterlevel i = j return totalwaterlevel"See full answer

Vishwanath K. - "DevOps Engineer Interview Questions for 3+ yrs experience candidate"See full answer

Shibin P. - "Effective loss functions for computer vision models vary depending on the specific task, some commonly used loss functions for different tasks: Classification Cross-Entropy Loss:Used for multi-class classification tasks. Measures the difference between the predicted probability distribution and the true distribution. Binary Cross-Entropy Loss:Used for binary classification tasks. Evaluates the performance of a model by comparing predicted probabilities to the true binary labe"See full answer

Matt M. - "Given the dataset does not contain many labels, it implies we cannot directly use supervised learning. I would ask more about the type of dataset we are given. Is it images, text, etc? This may inform the types of transformations we do the dataset. I can see two approaches to training Given the labels we do have, we can find a method to generate labels for the other unlabeled data. This likely will introduce some error since they may not be true labels, but it at least allows processing the"See full answer

Madhur K. - "I have worked with ML when I was a PM for Customer Due Diligence and it required recalibration to improve the matching logic (for screening and Proof of business matching with Google) and threshold analysis. In this scenario we had to extract past True positive, False positive decisions from operations and run threshold analysis to come up with a new threshold to match better and remove the noise i.e. False positives. Taking this new threshold for matching, the ML algorithm is receiving feedback"See full answer