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10+ KVP Business Solutions Interview Questions and Answers
Q1. K-th Permutation Sequence of First N Natural Numbers
Determine the K-th permutation from a sequence of the first N natural numbers.
Input:
Integer T indicating the number of test cases followed by 'T' test case...read more
The task is to find the Kth permutation of the sequence of first N natural numbers.
Generate all possible permutations of the sequence of first N natural numbers
Sort the permutations in lexicographical order
Return the Kth permutation from the sorted list
Q2. Palindromic Substrings Problem Statement
You are given a string 'STR'. Your task is to determine the total number of palindromic substrings present in 'STR'.
Example:
Input:
"abbc"
Output:
5
Explanation:
The pa...read more
The task is to find the total number of palindromic substrings in a given string.
Iterate through each character in the string and consider it as the center of a potential palindrome
Expand outwards from the center to check if the substring is a palindrome
Count the number of palindromic substrings found
Q3. Longest Common Subsequence Problem Statement
Given two strings, S
and T
with respective lengths M
and N
, your task is to determine the length of their longest common subsequence.
A subsequence is a sequence tha...read more
The problem is to find the length of the longest common subsequence between two given strings.
A subsequence is a string that can be derived from another string by deleting some or no characters without changing the order of the remaining characters.
We can solve this problem using dynamic programming.
Create a 2D array to store the lengths of the longest common subsequences for all possible prefixes of the two strings.
Iterate through the strings and fill the array based on the ...read more
Q4. Guestimates-"How many Ola cab runs in a day in Chennai"?
It is difficult to provide an exact number, but there are likely thousands of Ola cab runs in Chennai each day.
The number of Ola cab runs in Chennai can vary depending on factors such as time of day, day of the week, and demand.
During peak hours, there may be a higher number of Ola cab runs as people commute to work or travel within the city.
On weekends or holidays, the number of Ola cab runs may increase due to leisure activities or tourism.
Ola's popularity and availability ...read more
Q5. How does OTP less work make a systematic diagram for it/
OTP less work eliminates the need for one-time passwords for authentication.
OTP less work uses alternative methods like biometrics, push notifications, or hardware tokens for authentication.
It provides a more seamless and user-friendly authentication experience.
Examples include fingerprint scanning on smartphones or security keys for two-factor authentication.
Q6. array with elements that represent buildings and their heights, find buildings that have a lakeside view
Iterate through the array and find buildings with a view of the lake based on their heights.
Iterate through the array of buildings
Compare the height of each building with the buildings on its right to determine if it has a lakeside view
Keep track of buildings with a lakeside view
Q7. Difference between logistic regression and svm
Logistic regression is a linear model used for binary classification, while SVM is a non-linear model that can handle complex decision boundaries.
Logistic regression is a probabilistic model that predicts the probability of a binary outcome based on input features.
SVM aims to find the hyperplane that best separates the classes in a high-dimensional space.
Logistic regression is more interpretable and easier to implement, while SVM can handle non-linear decision boundaries thro...read more
Q8. What is LLM how do you fine tune them
LLM stands for Language Model. Fine tuning involves adjusting hyperparameters and training on specific data.
LLM stands for Language Model, which is a type of AI model that predicts the next word in a sentence.
Fine tuning LLM involves adjusting hyperparameters such as learning rate, batch size, and number of training epochs.
Fine tuning also involves training the LLM on specific data related to the task at hand, such as medical texts for medical applications.
Examples of LLM inc...read more
Q9. What is RAG and its working?
RAG stands for Red, Amber, Green and is a project management tool used to visually indicate the status of tasks or projects.
RAG is commonly used in project management to quickly communicate the status of tasks or projects.
Red typically indicates tasks or projects that are behind schedule or at risk, Amber indicates tasks that are on track but may need attention, and Green indicates tasks that are on schedule or completed.
RAG can be used in various project management tools or ...read more
Q10. how would ypu plan a event calender
To plan an event calendar, consider the purpose of the events, target audience, available resources, and desired outcomes.
Identify the purpose of the events (e.g. team building, training, celebrations)
Consider the target audience and their preferences (e.g. employees, clients, stakeholders)
Allocate resources such as budget, venue, speakers, and equipment
Set clear goals and desired outcomes for each event
Create a timeline with key milestones and deadlines
Promote the events thr...read more
Q11. Longest sub array question in python.
Find the longest subarray of strings in a given array.
Iterate through the array and keep track of the current subarray length.
Reset the subarray length when encountering a non-string element.
Return the length of the longest subarray found.
Q12. Explain architecture of Efficient-nets
EfficientNets are a family of convolutional neural networks that have been designed to achieve state-of-the-art accuracy with fewer parameters and FLOPS.
EfficientNets use a compound scaling method to balance network depth, width, and resolution for optimal performance.
They are based on a baseline network architecture called EfficientNet-B0, which is then scaled up to create larger models like EfficientNet-B1, EfficientNet-B2, and so on.
EfficientNets have been shown to outperf...read more
Q13. Explain transformers architecture
Transformers architecture is a deep learning model that uses self-attention mechanism to process sequential data.
Transformers consist of an encoder and a decoder, each composed of multiple layers of self-attention and feed-forward neural networks.
Self-attention mechanism allows the model to weigh the importance of different input tokens when making predictions.
Transformers have achieved state-of-the-art performance in various natural language processing tasks, such as machine...read more
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