Amazon Machine Learning Engineer Interview Questions

Anonymous Roadrunner - "from typing import List def two_sum(nums: List[int], target: int) -> List[int]: prevMap = {} for i, n in enumerate(nums): diff = target - n if diff in prevMap: return [prevMap[diff], i] else: prevMap[n] = i return [] debug your code below print(two_sum([2, 7, 11, 15], 9)) `"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

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

Shakshi R. - "def buildTree(self, preorder: List[int], inorder: List[int]) -> Optional[TreeNode]: if not preorder or not inorder: return None root = TreeNode(preorder[0]) mid = inorder.index(preorder[0]) root.left = self.buildTree(preorder[1:mid+1], inorder[:mid]) root.right = self.buildTree(preorder[mid+1:], inorder[mid+1:]) return root"See full answer

Anonymous Roadrunner - "from typing import List def traprainwater(height: List[int]) -> int: if not height: return 0 l, r = 0, len(height) - 1 leftMax, rightMax = height[l], height[r] res = 0 while l < r: if leftMax < rightMax: l += 1 leftMax = max(leftMax, height[l]) res += leftMax - height[l] else: r -= 1 rightMax = max(rightMax, height[r]) "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

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

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