Free Python Institute PCED-30-01 Exam Questions

Become Python Institute Certified with updated PCED-30-01 exam questions and correct answers

Page: 1 / 77

Total 382 Questions | Updated On: Aug 18, 2026

Add To Cart

Question 1

You discover that a text-based column in your DataFrame supposed to contain only alphanumeric characters has special characters in some entries. This constitutes "bad data" for your specific use-case. How can you most effectively clean this column?

A : Convert the entire DataFrame to a NumPy array and handle text cleaning

B : Use Regular Expressions with df.str.replace() to remove all special characters at once

C : Use df.replace() to replace each special character individually

D : Drop the column to avoid any complications

Answer: B

Question 2

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?

A : Seaborn, Statsmodels, OpenCV

B : Pandas, Matplotlib, Keras

C : Scikit-learn, Matplotlib, BeautifulSoup

D : NumPy, Scikit-image, TensorFlow

Answer: D

Question 3

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?

A : Plotly

B : Geopandas

C : Seaborn

D : Matplotlib

Answer: B

Question 4

You are given a JSON file containing social data about users' interactions on a website. The JSON object includes an array named likes that should contain integers representing the number of likes each post has received. Which of the following Python code snippets would be most appropriate to validate that all elements in the likes array are indeed integers?

A : import json data = json.load(open('data.json')) if len(data['likes']) > 0: print("Data is valid")else: print("Data is invalid")

B : import json data = json.load(open('data.json')) if all(isinstance(x, int) for x in data['likes']): print("Data is valid")else: print("Data is invalid")

C : import json data = json.loads('data.json') if isinstance(data['likes'], list): print("Data is valid")else: print("Data is invalid")

D : import json data = json.load(open('data.json')) if 'likes' in data.keys(): print("Data is valid")else: print("Data is invalid")

Answer: B

Question 5

In a regression analysis of employee job satisfaction against years of experience, the p-value for the 'years of experience' variable is found to be 0.12. How should you interpret this result at a 0.05 significance level?

A : A p-value of 0.12 confirms that years of experience and job satisfaction are positively correlated.

B : Years of experience is not a statistically significant predictor of job satisfaction.

C : Years of experience is a statistically significant predictor of job satisfaction.

D : A p-value of 0.12 means that years of experience has a 12% impact on job satisfaction.

Answer: B

Page: 1 / 77

Total 382 Questions | Updated On: Aug 18, 2026

Add To Cart