Free Python Institute PCED-30-01 Exam Questions
Absolute Free PCED-30-01 Exam Practice for Comprehensive Preparation
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Python Institute PCED-30-01 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: Sep 25, 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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Which of the following cross-validation techniques is most appropriate when you are working with a dataset where the target variable's classes are imbalanced?
Answer: C
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Which of the following cross-validation techniques is most appropriate when you are working with a dataset where the target variable's classes are imbalanced?
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Question 2
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You have a linear regression model with 5 features. After checking the VIF (Variance Inflation Factor) for multicollinearity, one of the features has a VIF of 12. What should you consider doing?
Answer: B
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You have a linear regression model with 5 features. After checking the VIF (Variance Inflation Factor) for multicollinearity, one of the features has a VIF of 12. What should you consider doing?
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Question 3
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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?
Answer: B
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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?
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Question 4
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You're developing a Python script to collect real-time data from various weather stations. Which approach would best help you implement robust data validation procedures to ensure the reliability and accuracy of the data?
Answer: C
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You're developing a Python script to collect real-time data from various weather stations. Which approach would best help you implement robust data validation procedures to ensure the reliability and accuracy of the data?
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Question 5
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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: