Question 1

You are working on a project that requires real-time data collection from a WebSocket API. Which of the following Python libraries would be most appropriate for this task?

Options :

A : csv

B : websocket-client

C : urllib

D : scrapy

Answer: B

Question 2

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 3

In a Pandas DataFrame, what would be the most efficient way to group the data by one column and apply multiple aggregate functions?

Options :

A : df.groupby('column').agg(['sum', 'mean'])

B : df.groupby('column').apply(lambda x: x.sum())

df.groupby('column').apply(lambda x: x.mean())

C : df.groupby('column').sum()

df.groupby('column').mean()

D : df['column'].sum()

df['column'].mean()

Answer: A

Question 4

Your Python script is giving you unexpected results in data analysis. Which of the following is generally not advisable for debugging this issue?

Options :

A : Review and validate data inputs and transformations

B : Use conditional breakpoints to isolate the problem

C : Examine the Python traceback to identify where the error originates

D : Suppress all exceptions using a general except block

Answer: D

Question 5

You are working on a machine learning project where you need to build a model for image classification. The project also requires heavy numerical computations and data preprocessing. Which of the following Python libraries would be most suitable to complete your project successfully?

Options :

A : Seaborn, Statsmodels, OpenCV

B : Pandas, Matplotlib, Keras

C : Scikit-learn, Matplotlib, BeautifulSoup

D : NumPy, Scikit-image, TensorFlow

Answer: D

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