Cognizant Data Scientist Interview Questions and Answers

PCA is a dimensionality reduction technique that transforms data into a lower-dimensional space while preserving variance.

• PCA identifies the directions (principal components) in which the data varies the most.

• It computes the covariance matrix of the data to understand how variables relate to one another.

• Eigenvalues and eigenvectors are derived from the covariance matrix to determine the principal components.

• Data is pro...read more

Strategies to combat overfitting and underfitting include regularization, cross-validation, and model selection.

• Use regularization techniques like L1 (Lasso) and L2 (Ridge) to penalize complex models.

• Implement cross-validation to ensure the model generalizes well to unseen data.

• Choose simpler models when overfitting is detected, such as linear regression instead of polynomial regression.

• Increase training data to help t...read more

Feature selection improves model performance by reducing overfitting, enhancing interpretability, and speeding up training.

• Reduces overfitting: Fewer features lead to simpler models that generalize better. Example: Using only age and blood pressure in a health model.

• Enhances interpretability: Fewer features make it easier to understand model decisions. Example: A model with 3 features is easier to explain than one with...read more

Retrieval-Augmented Generation combines retrieval of relevant data with generative models to enhance response quality.

• Retrieval-Augmented Generation (RAG) uses a two-step process: retrieval of relevant documents followed by generation of responses.

• In the retrieval phase, a model searches a large database for documents that are contextually relevant to the input query.

• The generative model then uses the retrieved documen...read more

A vector database stores and manages high-dimensional vectors for efficient similarity search and retrieval.

• Designed for handling embeddings from machine learning models.

• Supports operations like nearest neighbor search for similarity.

• Examples include Pinecone, Weaviate, and Milvus.

• Useful in applications like recommendation systems and image retrieval.

Learning factor, assumptions for linear regression, model evaluation, bias-variance trade off, gradient descent, AUC-ROC curve, Python function writing.

• Learning factor refers to the rate at which a model learns from the data, often used in gradient descent algorithms.

• Assumptions for linear regression include linearity, independence, homoscedasticity, and normality of residuals.

• Model evaluation involves metrics like mea...read more

I am a data scientist with a strong background in statistics and machine learning, passionate about solving complex problems using data-driven approaches.

• Completed a Master's degree in Data Science from XYZ University

• Proficient in programming languages such as Python, R, and SQL

• Experience in building predictive models and conducting data analysis

• Strong communication skills to present findings to non-technical stakehold...read more