Derived from 1 submission for ALL. Format details may vary across different teams or locations.
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The candidate participated in two technical interviews for the ML Engineer Intern role at Quantrium. The interviews covered a broad range of topics including Python programming, machine learning fundamentals, deep learning concepts, and problem-solving questions. The candidate was asked to solve coding problems and explain theoretical concepts.
Questions focused on OOPs concepts, Python-specific topics (decorators, magic methods, list comprehension, negative indexing, enumerate, zipping, broadcasting), machine learning fundamentals (precision, recall, evaluation metrics, k-means, reinforcement learning, decision trees, kernel trick, SVM), and a coding question on Roman numerals to integers conversion.
Explain the underlying concepts of OOPs.
What are decorators in Python?
What are magic methods in Python?
Difference between class attribute and instance attribute.
Difference between precision and recall.
Evaluation metrics in unsupervised learning (e.g., silhouette coefficient).
Evaluation metrics in machine learning.
What is list comprehension in Python?
What is negative indexing in Python?
Reverse a string in Python using negative indexing.
Explain the k-means algorithm.
What is Reinforcement Learning?
Explain states, actions, and rewards in reinforcement learning.
Type of problem object detection belongs to (classification or regression).
Explain any ML projects done or being done.
What is enumerate and zipping in Python?
What is a dataframe?
How to create a dataframe in Python?
What are CSV files?
Methods used with dataframes.
What are decision trees?
What is a series in Python?
What is the kernel trick in SVM?
What is broadcasting in Python?
Coding question: Convert Roman numerals to integers (LeetCode problem).
Questions focused on deep learning (dropout layers, neural network weight initialization, overfitting, underfitting, dense vs. convolutional layers), Python concepts (mutable vs. immutable), a coding question on converting integers to Roman numerals, and an aptitude question involving average salary calculations.
What are dropout layers in deep learning?
What happens when all weights are initialized to 1 in a neural network?
What is overfitting and underfitting?
Output of a given Python code snippet involving loops and conditionals.
Coding question: Convert integers to Roman numerals (LeetCode problem).
Aptitude question: Average salary calculation for employees in two departments with given constraints.
What is mutable and immutable in Python?
Difference between dense layers and convolutional layers.
InterviewDigest