Desafios específicos no meu dia a dia que resolvi com Python. Meu nível de proficiência em Python
Sr Data Scientist Interview Questions
3,390 sr data scientist interview questions shared by candidates
The questions were mostly on basics of statistics and machine learning
Difference between RAG and Agentic RAG How to evaluate and improve the performance of a deployed model/agent
Round 1: Breadth Assessment This round evaluated the width of my knowledge across the Data Science spectrum. The structure was: Personal introduction Project walkthrough (one detailed project explanation) Technical questions spanning: Machine Learning: Data preprocessing and model evaluation Deep Learning: Optimizers and Gradient Descent Generative AI: RAG (Retrieval-Augmented Generation) and LLMs Coding problems: Printing series patterns and list/dictionary comprehension Difficulty level: Easy to moderate. Round 2: Deep Dive Technical Round This round went significantly deeper into specialized topics: Sentence transformers and their applications Benchmarking and evaluation methodologies RAG architecture and implementation Evaluation frameworks (RAGAs, DSPy) Transformer architecture fundamentals Advanced concepts: Training different word embeddings, contextual awareness, positional encoding
Bayes Rule and SVM
Tell us how you would tackle problem X.
1. Explain entropy and examples. 2. How will you design a system of digital art price prediction.
1.Basic of statatistics 2.Basic of ML 3.Basic of EDA
Explain current project in detail
Usual Machine Learning conceptual questions
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