Me postulé a través de una facultad o universidad. El proceso tomó 2 meses. Acudí a una entrevista en UST (Chennai) en dic 2024
Entrevista
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).
Preguntas de entrevista [2]
Pregunta 1
diff btw recall vs precision in-terms of real life.
Me postulé a través de una facultad o universidad. Acudí a una entrevista en UST (Coimbatore) en nov 2025
Entrevista
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.
Preguntas de entrevista [1]
Pregunta 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.
Me postulé a través de una facultad o universidad. El proceso tomó 2 meses. Acudí a una entrevista en UST (Chennai) en dic 2024
Entrevista
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).