I recently had an interview that felt quite easy, yet I was rejected at the end—which honestly left me a bit frustrated and disappointed.

There were three technical rounds, and none of them felt particularly challenging.

• Round 1: Resume Deep Dive & Coding The first round focused on my resume—mainly past projects and experience. They asked me to explain a few things I’d worked on and justify some of the technical decisions I’d made. Then they gave me a fairly basic coding question, which I completed without any issues.
• Round 2: AI Fundamentals This round was centered around the basics of AI and ML. They asked conceptual questions—like the difference between supervised and unsupervised learning, overfitting vs. underfitting, and basic algorithm comparisons. It felt like more of a discussion than an evaluation. I answered everything confidently.
• Round 3: In-Depth AI/ML Discussion The third round went deeper into my understanding of AI/ML. They asked about the architectures I’ve used, hyperparameter tuning strategies, training challenges, model evaluation metrics, etc. Even this round wasn’t too tough—it was very aligned with the work I’ve done in the past.

Despite all of that going well, I was rejected. Based on how easy and smooth the rounds were, I can’t help but feel like they had already chosen another candidate and were just interviewing others as a formality—to check a process box or complete internal requirements. It’s disappointing because it didn’t feel like a merit-based outcome.

What is regularization? Explain L1 vs L2.

How do you evaluate a classification model? What metrics do you prefer and why?