These are Fine-Tuning questions of the kind Infosys actually asks — the patterns that keep coming back in Infosys's AI project interviews, where what matters is applying Fine-Tuning sensibly for clients rather than research depth. Treat this page as a mock interview: say every answer out loud before revealing it. If one surprises you, the lesson behind it is linked at the bottom.
Infosys Fine-Tuning concept questions
What is LoRA, and why did it become the standard way to fine-tune?
Asked in
How would you prepare a dataset for fine-tuning a support-reply model?
Asked in
Infosys Fine-Tuning applied & hands-on questions
A junior colleague proposes: 'We'll fine-tune on 120 examples for 10 epochs, evaluate on the training set since we don't have spare data, and deploy Monday.' List what is wrong and what you would change.
Asked in
How to use this page: Infosys rarely asks something you've never seen — they ask a standard Fine-Tuning concept and then push one level deeper ("why?", "what would you do if..."). Master the concept in the Fine-Tuning course lessons, and the follow-up stops being scary.
Keep practising: When to Fine-Tune, LoRA & PEFT, Evaluating LLMs and LLMOps & Monitoring cover what most Infosys Fine-Tuning rounds test — and the Infosys exam guide covers the rest of their selection process.