Wipro interview questions

Python: Sum of Diagonals of a Matrix How to Evaluate LLM or RAG Different Evaluation metrics Types of RAG how to evaluate LLM model output Difference between Feedforward network and ANN Difference between Bagging and boosting Working of Xgboost Adaboost Gradient Boost Difference between various Boosting models Types of Embeddings and mathematics What is Encoder in transformer Difference between Self Attention and Masked Attention Working of LSTM, RNN Latency of RAG system How to handle Hallucinations in Gen AI Different ways to do so What if we Normalise at Layer instead of Batch Normalisation Assumptions of Linear Regression and how to Handle them , How to check them if passed or not Homoscedasticity vs Heteroscadasticity What to do if not passed What to do on the Data side and Algorithm side Different Methods and Mathematics of Embeddings Guardrails for LLM How they work How to create Guardrails Different tools for Guardrails How to train them Types of RAG, Attention mechanisms, Guardrails, Embeddings, Retrieval Methods How to get relevant data How to do Searching from Embeddings Classical ML, Maths behind ML algo, Behaviour of ML algos, Data Preprocessing, Stats Different ways of Handling Hallucinations How to prevent from passing sensitive information to LLM