Teradata

Role: AI/ML Intern

College: Not Specified (University's on-campus drive)

Interview Date: Not Specified (Recently interviewed, article published September 16, 2025)

Interview Type: On-Campus, Internship

Result: Shortlisted for interview rounds (after OA), solved both coding questions in Technical Round 1.

Difficulty: Online Assessment: Easy-Medium to Challenging; Technical Round: Easy DSA questions.

Rounds: 3 rounds (Online Assessment, Technical Round 1, Design Round 2)

Topics Asked: Data Structures (Queue, Priority Queue, DFS, BFS), Algorithms (Two-Pointers, Graphs, Iterative/Recursive Approaches), Projects, System Design (Project initiation, next steps, error handling, logging)

Experience:

The candidate interviewed with Teradata for an AI/ML Intern position through their university's on-campus drive. The selection process involved an online assessment followed by two interview rounds.

Round 1: Online Assessment
The assessment comprised 3 coding problems and a second part where the candidate had to write an explanation for their solution to the second problem. The candidate solved two out of three problems and was shortlisted.

• Coding Problem 1: An easy-to-medium level two-pointers problem.
• Coding Problem 2: A slightly harder and somewhat ad hoc problem.
• Coding Problem 3: A quite challenging graphs problem, possibly intended to be solved using binary lifting.

Round 2: Technical Round (Slightly more than 1 hour)
This round focused on the candidate's resume and Data Structures and Algorithms (DSA) problems. The interviewer asked several questions about the candidate's projects and easy DSA-based questions.

• DSA Questions: Difference between a queue and a priority queue, and the difference between DFS and BFS.
• Coding Question 1: Print factorial of a number using both iterative and recursive approaches.
• Coding Question 2: Print the boundary traversal of a binary tree.

The candidate solved both coding questions and was shortlisted for the next round.

Round 3: Design Round (About an hour)
This round assessed the candidate's knowledge of the design aspect of a project.

• Design Questions:
• How do you start working on a project?
• Once you have figured out the problem statement, what are the next steps you take?
• Several questions related to error handling and logging. (Candidate noted not being proficient in these topics).

Tips: ML knowledge was not required for this AI/ML role; strong problem-solving skills were sufficient. Competitive programming experience is beneficial for the OA. Prepare resume and projects thoroughly. Strong knowledge of OOP, OS, and System Design is a plus.