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Duration: ~40 minutes
The interview was a mix of HR, Quantitative Aptitude, Statistics, AI/ML, and General Discussion.
You have 1000 coins, where:
999 are fair (unbiased) coins. 1 is a biased coin with heads on both sides.
You randomly pick one coin from the collection and toss it 10 times. Every toss results in Heads.
Question: What is the probability that the selected coin is the biased coin?
The interviewer asked questions on fundamental statistics concepts, including:
Random Variables Covariance Expectation Variance Basic probability concepts
Most questions were conceptual rather than calculation-heavy.
The discussion covered several GenAI and ML topics, including:
MVP MCP LLMs Hallucinations in Large Language Models Vector Databases RAG (Retrieval-Augmented Generation) Semantic Search APIs General AI/ML fundamentals
Most questions focused on understanding the concepts instead of implementation details.
Some of the HR questions asked were:
What is the most weird or wrong decision you have taken so far, and how did you overcome it? If you make three wrong decisions consecutively, what would you think before making the fourth decision? Tell me about yourself. General discussion about projects and background.
Overall, the interview was balanced and focused on evaluating problem-solving ability, conceptual knowledge, and decision-making rather than asking very difficult technical questions.