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: Apr 01, 2026

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Question 1

While working with a dataset, you notice some outliers that are not errors but extreme values that are relevant to your analysis. How should you handle them for ensuring the reliability and accuracy of your Python data analysis?

A : Ignore the outliers as they are not errors

B : Apply a log transformation

C : Replace the outliers with median values

D : Remove the outliers

Answer: B

Question 2

What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?

A : PCA is non-linear while t-SNE is linear

B : PCA is more computationally efficient for large datasets

C : PCA retains local structure while t-SNE focuses on global structure

D : PCA gives a more accurate representation of distances between clusters

Answer: B

Question 3

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 4

Which of the following NumPy methods would you use to apply a function element-wise on a 1-D array, while maintaining the shape of the original array?

A : np.reshape()

B : np.apply_along_axis()

C : np.ravel()

D : np.vectorize()

Answer: D

Question 5

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

Page: 1 / 77

Total 382 Questions | Updated On: Apr 01, 2026

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