WWT | Data Science - Intern | Interview Experience | OnCampus
Summary
I attended a three‑round interview for a Data Science Intern position at WWT, which included technical rounds covering ML concepts, coding tasks, and a puzzle, followed by an HR round.
Full Experience
Round 1 - Resume based
Introduction. Little Resume projects description.
I has RAG so I was asked about different types of evaluation parameters like MRR and NDCG, precision and recall and when to use when
Bagging and Boosting techniques, how to reduce overfitting. Bias and variance tradeoff in ML.
Explain backpropagation and softmax function.
Pandas code to find count of null values, SQL code to find emails that appear more than once.
Puzzle - You have 9 balls 8 are identical and 1 is heavier, find minimum times you use weighing scale and find the ball.
Round 2 - DL
I only got round 1 but I asked other 3 people who got round 2, all were asked about PyTorch and other libraries first then a puzzle simillar to round 1.
High concepts of RAG intended towards its system design - prompt caching, multiple prompts to handle same service?
If there is a model with single feature and has 90% accuracy what can you predict and what is the relibability explain everything you can think off.
Round 3 - HR
No idea about this one, only 1 person got this on my campus.
Interview Questions (4)
Pandas code to count null values
Write Pandas code to find the count of null values in a DataFrame.
SQL query for duplicate emails
Write an SQL query to find email addresses that appear more than once in a table.
9 balls weighing puzzle
You have 9 balls, 8 are identical and 1 is heavier. Find the minimum number of weighings needed to identify the heavier ball using a balance scale.
Model with single feature and 90% accuracy
If there is a model with a single feature and it achieves 90% accuracy, what can you predict from it and how reliable is that prediction? Explain your reasoning.