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 dataset of a retail company's sales transactions, which includes details like customer ID, transaction amount, transaction date, and store ID. You're tasked with summarizing monthly revenue for each store. What would be the most appropriate data aggregation technique?

Options :

A : Group the data by store ID and sum the transaction amounts daily, then take the average to get the monthly revenue.

B : Group the data by customer ID and store ID, then sum the transaction amounts and divide by the number of unique customers for each month.

C : Group the data by transaction date and calculate the daily revenue, then aggregate this to the monthly level without considering store ID.

D : Group the data by store ID and sum the transaction amounts for each month.

Answer: D

Question 3

You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

Options :

A : Perform clustering on the dataset

B : Apply a linear regression model to reduce dimensions

C : Use a scatter plot to visualize all the features

D : Standardize the dataset

Answer: D

Question 4

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

Question 5

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

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