Machine Learning Interview Questions Quiz (Advanced Level )

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Machine Learning Advanced Level Interview Questions Quiz

1 / 15

What is the role of Lagrange multipliers in Support Vector Machines

To minimize bias

To enforce constraints in the optimization problem

To normalize the feature values

To reduce overfitting

2 / 15

What is the purpose of the Hessian matrix in second-order optimization

It measures gradient direction

It determines model complexity

It provides curvature information

It computes residuals

3 / 15

What is the KKT condition used for in machine learning optimization

Model evaluation

Regularization tuning

Hyperparameter selection

Solving constrained optimization problems

4 / 15

Which of the following is true about Markov Chain Monte Carlo methods

They assume Gaussian distribution

They require labeled data

They are used for approximating complex distributions

They are deterministic algorithms

5 / 15

What is the primary purpose of variational inference in probabilistic models

Parameter tuning

Finding closed-form solutions

Approximating posterior distributions

Calculating loss functions

6 / 15

In Bayesian learning, what is the significance of the prior distribution

It represents the observed data

It quantifies uncertainty in predictions

It encodes prior beliefs before seeing the data

It is equivalent to the likelihood function

7 / 15

Why is dropout used in deep learning models

To increase training speed

To make the model deeper

To avoid vanishing gradients

To prevent overfitting by randomly deactivating neurons

8 / 15

Which of the following is true about attention mechanisms in neural networks

They reduce model complexity

They allow the model to focus on relevant parts of input

They replace activation functions

They are used only in CNNs

9 / 15

What is the vanishing gradient problem in deep networks

Gradients explode in magnitude

Gradients diminish and fail to update weights effectively

Model fails to generalize

Loss function becomes undefined

10 / 15

What is the purpose of batch normalization

To apply dropout

To reduce model depth

To stabilize learning and accelerate training

To compute loss

11 / 15

Which metric is most appropriate for imbalanced classification problems

Accuracy

Mean Absolute Error

Precision Recall AUC

R squared

12 / 15

What is the purpose of the ELBO in variational autoencoders

To maximize reconstruction loss

To approximate true posterior using tractable distributions

To minimize model parameters

To visualize hidden layers

13 / 15

Why are residual connections used in deep networks

To increase regularization

To reduce backpropagation errors

To allow gradient flow through identity mappings

To increase dropout rate

14 / 15

Which concept allows BERT to understand context in both directions

Recurrent encoding

Bidirectional attention

Linear regression

Dropout

15 / 15

What is a key benefit of using ensemble learning

It simplifies the model

It guarantees perfect accuracy

It combines predictions to improve robustness and accuracy

It replaces deep networks

Your score is

The average score is 46%

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