I applied through university. I interviewed at UST (Coimbatore) in Nov 2025
Interview
After resume shortlisting, attended a coding exam in college campus. Both in MCQ and coding question section, Mahine Learning and Deep Learning focused question were asked. No aptitude and others.
Interview questions [1]
Question 1
Coding questions were simply linear regression and knn questions but some descriptions in knn question was not clear. all mcq questions were about features and functions in various deep learning, neural network models.
I applied through university. The process took 2 months. I interviewed at UST (Chennai) in Dec 2024
Interview
The interview process spanned 2 months and had 5 rounds, starting with 2700 participants, with only (5+3) final selections.
Round 1: Conducted on the WeCP platform (150 mins). It included:
Coding: 3 problems (e.g., Sieve of Eratosthenes, Maximum Sum Subarray).
Debugging: 2 simple C++ questions.
MCQs: 30 questions (verbal, reasoning, numerical).
Round 2: Focused on ML concepts (coding and MCQs).
Coding: Logistic regression and WCSS computation in Python.
MCQs: Advanced and In-depth ML/DL topics.
Round 3: Technical discussion involving project explanation, case studies (e.g., retail data analysis), and live data insights generation (under 15 mins).
Round 4: Resume walkthrough, project deep dives, and ML evaluation metrics like recall vs precision.
Round 5: HR round covering personal background, interests, and industry-relevant topics like Explainable AI (XAI).
Interview questions [2]
Question 1
diff btw recall vs precision in-terms of real life.
I applied through university. The process took 2 months. I interviewed at UST (Chennai) in Dec 2024
Interview
The interview process spanned 2 months and had 5 rounds, starting with 2700 participants, with only (5+3) final selections.
Round 1: Conducted on the WeCP platform (150 mins). It included:
Coding: 3 problems (e.g., Sieve of Eratosthenes, Maximum Sum Subarray).
Debugging: 2 simple C++ questions.
MCQs: 30 questions (verbal, reasoning, numerical).
Round 2: Focused on ML concepts (coding and MCQs).
Coding: Logistic regression and WCSS computation in Python.
MCQs: Advanced and In-depth ML/DL topics.
Round 3: Technical discussion involving project explanation, case studies (e.g., retail data analysis), and live data insights generation (under 15 mins).
Round 4: Resume walkthrough, project deep dives, and ML evaluation metrics like recall vs precision.
Round 5: HR round covering personal background, interests, and industry-relevant topics like Explainable AI (XAI).
