Question 1

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

Question 2

You have a Pandas DataFrame that contains missing values. Which of the following methods will replace all NaNs with zeros?

Options :

A : df.fillna(value=0)

B : df.replace(to_replace=np.nan, value=0)

C : df['column_name'] = 0

D : df.dropna(subset=['column_name'])

Answer: A

Question 3

Which of the following NumPy methods would you use to apply a function element-wise on a 1-D array, while maintaining the shape of the original array?

Options :

A : np.reshape()

B : np.apply_along_axis()

C : np.ravel()

D : np.vectorize()

Answer: D

Question 4

You have a linear regression model with 5 features. After checking the VIF (Variance Inflation Factor) for multicollinearity, one of the features has a VIF of 12. What should you consider doing?

Options :

A : Increase the sample size to improve the model.

B : Remove the feature with the high VIF from the model.

C : Use L1 regularization to mitigate the effect of multicollinearity.

D : Ignore it, as VIF of 12 is acceptable.

Answer: B

Question 5

You are a data analyst who needs to collect large volumes of data from a RESTful API that has pagination implemented. Which Python code snippet would be the most effective way to gather all the available data?

Options :

A : import requests

page = 1

while True:

response = requests.get(f"https://api.example.com/data?page={page}")

if response.status_code != 200:

break

data = response.json()

# Process data

page += 1

B : import requests

response = requests.get("https://api.example.com/data")

data = response.json()

C : import requests

pages = [1, 2, 3, 4, 5]

for page in pages:

response = requests.get(f"https://api.example.com/data?page={page}")

data = response.json()

# Process data

D : import requests

for page in range(1, 100):

response = requests.get(f"https://api.example.com/data?page={page}")

data = response.json()

# Process data

Answer: A

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