Free Python Institute PCED Exam Questions
Absolute Free PCED Exam Practice for Comprehensive Preparation
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Python Institute PCED Exam Questions
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Provided By: Python Institute
- Exam: Certified Entry-Level Data Analyst with Python
- Certification: PCED
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Total Questions: 382
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Updated On: Jan 12, 2026
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Rated: 4.9 |
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Online Users: 764
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Question 1
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You have the following Pandas DataFrame with some missing values:
import pandas as pd
import numpy as np
df = pd.DataFrame(
{"A": [1, np.nan, 3], "B": [4, 5, np.nan], "C": [7, 8, 9]}
)
How would you fill the missing values in column 'A' with the mean value of that column?
Answer: B
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You have the following Pandas DataFrame with some missing values:
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Question 2
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You are working with a logistic regression model for a binary classification problem. You notice that the training error is significantly lower than the validation error. What could you do to improve the model's performance on the validation set?
Answer: D
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You are working with a logistic regression model for a binary classification problem. You notice that the training error is significantly lower than the validation error. What could you do to improve the model's performance on the validation set?
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Question 3
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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?
Answer: D
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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?
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Question 4
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What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?
Answer: B
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What is the primary reason to use PCA (Principal Component Analysis) over t-SNE when visualizing high-dimensional data?
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Question 5
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You are working with a dataset that includes both categorical and continuous features. Which of the following feature scaling techniques would be most appropriate for scaling only the continuous features?
Answer: A
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You are working with a dataset that includes both categorical and continuous features. Which of the following feature scaling techniques would be most appropriate for scaling only the continuous features?