Free Python Institute PCED Practice Exam Questions and Answers 2026

Start Learning with the Newest and 100% Free PCED Exam Dumps Questions

Page: 1 / 77

Total 382 Questions | Updated On: Apr 01, 2026

Add To Cart

Question 1

You are given a DataFrame df with a column 'Date' of type string in the format 'YYYY-MM-DD'. You want to filter the rows where the year is 2020. Which of the following code snippets is the most efficient way to do so?

A : df[pd.to_datetime(df['Date']).dt.year == 2020]

B : df[df['Date'].str.contains('2020')]

C : df[df['Date'].str.slice(0, 4) == '2020']

D : df[df['Date'].apply(lambda x: x.split('-')[0]) == '2020']

Answer: C

Question 2

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?

A : os

B : cProfile

C : sys

D : pickle

Answer: B

Question 3

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?

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 4

Which of the following libraries in Python is not commonly used for web scraping to collect data?

A : Scrapy

B : Selenium

C : Pandas

D : BeautifulSoup

Answer: C

Question 5

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

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

Page: 1 / 77

Total 382 Questions | Updated On: Apr 01, 2026

Add To Cart