Question 1

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 2

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 3

While working with a dataset, you notice some outliers that are not errors but extreme values that are relevant to your analysis. How should you handle them for ensuring the reliability and accuracy of your Python data analysis?

Options :

A : Ignore the outliers as they are not errors

B : Apply a log transformation

C : Replace the outliers with median values

D : Remove the outliers

Answer: B

Question 4

You are tasked with merging two datasets, df1 and df2, on a common column 'id'. df1 has a column 'value1' and df2 has a column 'value2'. After merging, you need to create a new column called 'total_value' that sums 'value1' and 'value2'. Which of the following code snippets accomplishes this?

Options :

A : df_merged = pd.concat([df1, df2], keys='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

B : df_merged = df1.merge(df2)

df_merged['total_value'] = df1['value1'] + df2['value2']

C : df_merged = df1.join(df2, on='id')

df_merged['total_value'] = df1['value1'] + df2['value2']

D : df_merged = pd.merge(df1, df2, on='id')

df_merged['total_value'] = df_merged['value1'] + df_merged['value2']

Answer: D

Question 5

Your dataset consists of geographical data points for different species of birds. You want to visualize the distribution of these species on a geographical map. Which Python library would be most appropriate for this kind of visualization?

Options :

A : Plotly

B : Geopandas

C : Seaborn

D : Matplotlib

Answer: B

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