Ho presentato la mia candidatura tramite l'università. La procedura ha richiesto 2 mesi. Ho sostenuto un colloquio presso UST (Chennai) nel mese di dic 2024
Colloquio
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).
Domande di colloquio [2]
Domanda 1
diff btw recall vs precision in-terms of real life.
Ho presentato la mia candidatura tramite l'università. Ho sostenuto un colloquio presso UST (Coimbatore) nel mese di nov 2025
Colloquio
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.
Domande di colloquio [1]
Domanda 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.
Ho presentato la mia candidatura tramite l'università. La procedura ha richiesto 2 mesi. Ho sostenuto un colloquio presso UST (Chennai) nel mese di dic 2024
Colloquio
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).
