Question 1

When using Python's requests library to collect data from a website through HTTP requests, which status code indicates that the requested resource has been moved permanently?

Options :

A : 403

B : 404

C : 200

D : 301

Answer: D

Question 2

You are working with a logistic regression model for a binary classification problem. You notice that the training error is significantly lower than the validation error. What could you do to improve the model's performance on the validation set?

Options :

A : Increase the model-s learning rate.

B : Add more features to the model.

C : Remove all features and retrain the model.

D : Regularize the model by introducing L1 or L2 penalties.

Answer: D

Question 3

You have the following Pandas DataFrame with some missing values:

import pandas as pd

import numpy as np

df = pd.DataFrame(

{"A": [1, np.nan, 3], "B": [4, 5, np.nan], "C": [7, 8, 9]}

)

How would you fill the missing values in column 'A' with the mean value of that column?

Options :

A : df['A'] = df['A'].fillna(df['C'].mean())

B : df['A'].fillna(df['A'].mean(), inplace=True)

C : df.fillna({'A': df['A'].mean()})

D : df['A'].apply(lambda x: x.fillna(x.mean()))

Answer: B

Question 4

You are evaluating a logistic regression model using the ROC curve. Which of the following is true regarding the Area Under the Curve (AUC)?

Options :

A : A model with an AUC of 0 performs better than one with an AUC of 1

B : A higher AUC indicates that the model has higher bias

C : A model with an AUC closer to 1 performs better than one closer to 0

D : A model with an AUC of 0.5 is an excellent model

Answer: C

Question 5

You have developed a logistic regression model for a classification task. After training, you notice that both the training and validation errors are high. Which of the following might be the problem with your model?

Options :

A : The learning rate is too high.

B : The model is overfitting the data.

C : The model is too complex.

D : The model is underfitting the data.

Answer: D

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