Technology & Programming

Challenging Data Science Test for Professionals

A rigorous, nine‑question assessment designed to gauge deep knowledge of data‑driven methods and computational techniques.

#programming#deep learning#machine learning#AI#statistics#data science#feature engineering#model evaluation#big data#algorithm

Quiz Intelligence

Total Questions

Difficulty Level

hard

Progress

0/0

Quiz Questions

Answer all questions below and test your knowledge.

1
In the bias‑variance decomposition, which statement correctly describes the trade‑off?
Question 1
2
Which evaluation metric is most suitable for a severely imbalanced binary classification problem?
Question 2
3
What is the primary effect of L1 regularization on linear model coefficients?
Question 3
4
For k‑means clustering, what is the computational complexity per iteration on n points, k clusters, and d dimensions?
Question 4
5
Which optimization algorithm guarantees convergence to the global optimum for a convex loss with L2 regularization, assuming appropriate step size?
Question 5
6
The main objective of Principal Component Analysis is to
Question 6
7
Which sampling strategy ensures that each class is represented proportionally in the training set?
Question 7
8
Which activation function is most prone to the vanishing‑gradient problem in deep networks?
Question 8
9
Which ensemble technique primarily reduces model variance while keeping bias roughly unchanged?
Question 9

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Challenging Data Science Test for Professionals

Quiz Intelligence

Quiz Questions

In the bias‑variance decomposition, which statement correctly describes the trade‑off?

Which evaluation metric is most suitable for a severely imbalanced binary classification problem?

What is the primary effect of L1 regularization on linear model coefficients?

For k‑means clustering, what is the computational complexity per iteration on n points, k clusters, and d dimensions?

Which optimization algorithm guarantees convergence to the global optimum for a convex loss with L2 regularization, assuming appropriate step size?

The main objective of Principal Component Analysis is to

Which sampling strategy ensures that each class is represented proportionally in the training set?

Which activation function is most prone to the vanishing‑gradient problem in deep networks?

Which ensemble technique primarily reduces model variance while keeping bias roughly unchanged?

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