Interview Questions

Jake M. - "Random Forest is a machine learning model used for classification problems or regression problems. It can handle binary classification as well as multi-class classification. It is a very efficient model and is great for a baseline or used in a service that needs extremely low latency depending on the size of the model. It's also a good option for wide datasets (dataset with many features) due to it's random subset of features. it is slightly less optimized for deep datasets on very large dataset"See full answer

Hemanth M. - "Start by giving a brief overview of the technical stacks used in our product. Highlight the key technologies, frameworks, languages, and tools employed. This sets the stage for a more detailed discussion. In choosing our technical stack, we prioritized scalability, opting for micro-services architecture to ensure flexibility and ease of scaling components independently. This decision enables us to handle increased user loads while maintaining optimal performance. One trade-off we made was favo"See full answer

Yash S. - "Run length encoding. This will preserve order of values in vector."See full answer

Shubhendu K. - "// array is sorted in non-increasing order // low = 0, high = arr.length - 1 int countOnes(int[] arr, int low, int high) { if (high >= low) { int mid = low + (high - low) / 2; if ((mid == high || arr[mid + 1] == 0) && (arr[mid] == 1)) { return mid + 1; } if (arr[mid] == 1) { return countOnes(arr, (mid + 1), high); } return countOnes(arr, low, (mid - 1)); } return 0; }"See full answer