Some concepts of Machine learning, LLM, RAG and databases.
Ai Developer Technology Engineer Interview Questions
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They asked about projects that I'd done.
Question similar to merge sort and analysis of its computational and storage complexity
Asked about specifics of current job, prompt engineering techniques. Open ended design questions
Research questions: dropout and batch norm, difference between training/inference time. Graph traversal with BFS.
The conversation started with typical questions such as “Tell me about yourself,” “What are your main interests,” “Why do you want to join us” . I was then asked to describe one of my previous projects in detail. After that, I was given a technical question about how I would approach clustering radar signals based on time series data. I began by outlining traditional time series approaches and then mentioned that I would also consider more advanced models. At that point, I was interrupted before I could go into further detail. In the feedback, I was told that I didn’t mention enough modern deep learning–based methods for time series data, which was cited as a reason for not moving forward.
Q-learning and DQN difference, Deadlocks and how to prevent them, RLock example, is python multithreading
Design a system that would allow an e-commerce website to track item views.
How would you scale this system to handle 1,000,000 monthly events
They asked, “How do you handle ambiguous data or unclear instructions while working on AI tasks?
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