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Research Foundation of State University New York AI Engineer Interview Questions

The questions to prepare for a Research Foundation of State University New York AI Engineer interview. Questions from real interview reports rank first. Updated weekly.

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1
Machine LearningStart here. 7 questions · ~69 min
Supervised vs Unsupervised LearningEasy

Explain how supervised and unsupervised learning differ, and ground the distinction in a practical ML example.

Unsupervised LearningFeature EngineeringBias-Variance TradeoffResearch Foundation of State University New York
Prevent Overfitting in ML ModelsEasy

Explain how to reduce overfitting using regularization, validation, and model selection.

Cross-ValidationBias-Variance TradeoffRegularizationResearch Foundation of State University New York
Handling Missing Values in MLEasy

Explain practical strategies for handling missing values in a supervised learning workflow, from diagnosis to modeling and validation.

Cross-ValidationFeature EngineeringRegularizationResearch Foundation of State University New York
Troubleshooting Underperforming ModelsMedium

Tests your debugging approach for model performance issues and iteration discipline.

Hyperparameter TuningCross-ValidationBias-Variance TradeoffResearch Foundation of State University New York
Implement Linear Regression in PythonMedium

Tests your ability to implement core ML algorithms and reason about training basics.

Feature EngineeringRegressionGradient DescentResearch Foundation of State University New York
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2
More topics3 questions · ~30 min
Implement Gradient DescentEasy
Practice

Implement batch gradient descent to fit a one-feature linear model for Plymouth Rock Assurance claim severity estimates.

MathArraysGradient DescentResearch Foundation of State University New York
Common Model Evaluation MetricsEasy

Explain common machine learning evaluation metrics and when each is useful.

PrecisionAccuracyRecallResearch Foundation of State University New York
Time Complexity of Sorting AlgorithmsEasy

Compare common sorting algorithms by best, average, and worst-case time complexity and explain when each is appropriate.

MathArraysSortingResearch Foundation of State University New York

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