Free Python Institute PCED Exam Questions

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

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Total 382 Questions | Updated On: Sep 25, 2026

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Question 1

What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?

A : Remove Rows with Missing Values

B : Mean Imputation

C : Linear Regression Imputation

D : Mode Imputation

Answer: D

Question 2

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?

A : Run a Principal Component Analysis (PCA) on the entire dataset.

B : Implement a Decision Tree algorithm to identify the most important features.

C : Use k-Nearest Neighbors (k-NN) to classify data points.

D : Utilize Natural Language Processing (NLP) for textual data and Min-Max scaling for numerical data.

Answer: D

Question 3

Suppose you are working on a classification problem with an imbalanced dataset. Which of the following techniques would not be effective for addressing the issue?

A : Using accuracy as the evaluation metric

B : Using the Synthetic Minority Over-sampling Technique (SMOTE)

C : Using different evaluation metrics such as F1-score.

D : Under-sampling the majority class

Answer: A

Question 4

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?

A : import requests page = 1while 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 5

You are pulling data from various databases using Python to compile a large dataset for analysis. How would you ensure the data’s accuracy and reliability?

A : Use the most recent data available in all databases.

B : Write the Python script in a way that it runs faster to save computational resources.

C : Compare a random subset of data with source records to validate accuracy.

D : Perform exploratory data analysis (EDA) only after data cleaning is done.

Answer: C

Page: 1 / 77

Total 382 Questions | Updated On: Sep 25, 2026

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