Question 1

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 2

You have a DataFrame df that contains duplicate rows. Your task is to remove duplicates based on two columns: 'A' and 'B'. What is the correct line of code to remove such duplicates while keeping the first occurrence?

Options :

A : df.drop_duplicates(subset=['A', 'B'], keep=False)

B : df.drop_duplicates()

C : df.unique(subset=['A', 'B'])

D : df.drop_duplicates(subset=['A', 'B'], keep='first')

Answer: D

Question 3

Your Python script is taking significantly longer than expected to complete. Which Python standard library could provide you with granular insights into the time taken by different parts of your code?

Options :

A : os

B : cProfile

C : sys

D : pickle

Answer: B

Question 4

You need to collect data from an Excel file with multiple sheets. One of the sheets is named "Sales" and another is named "Inventory". You want to load the "Sales" sheet into a DataFrame named df_sales and the "Inventory" sheet into a DataFrame named df_inventory. Which of the following code snippets accomplishes this?

Options :

A : xls = pd.Excel('file.xlsx')

df_sales = xls.parse('Sales')

df_inventory = xls.parse('Inventory')

B : df_sales = pd.read_excel('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_excel('file.xlsx', sheet_name='Inventory')

C : df_sales = pd.read_csv('file.xlsx', sheet_name='Sales')

df_inventory = pd.read_csv('file.xlsx', sheet_name='Inventory')

D : df_sales = pd.read_excel('file.xlsx').parse('Sales')

df_inventory = pd.read_excel('file.xlsx').parse('Inventory')

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