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: Aug 18, 2026

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Question 1

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 2

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 3

You are working with a large dataset containing multi-dimensional features related to customer behavior. The dataset is too large and complex to analyze easily. What technique could be most effective for reducing the dataset's dimensionality while retaining most of its original variance?

A : Principal Component Analysis (PCA)

B : One-hot encoding

C : Linear Discriminant Analysis (LDA)

D : Max-Min Scaling

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 have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

A : Perform clustering on the dataset

B : Apply a linear regression model to reduce dimensions

C : Use a scatter plot to visualize all the features

D : Standardize the dataset

Answer: D

Page: 1 / 77

Total 382 Questions | Updated On: Aug 18, 2026

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