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: Mar 11, 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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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 2
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Suppose you have a DataFrame df with columns Name, Age, Gender, and Salary. Which of the following code snippets will filter the DataFrame to include only rows where Age is more than 25 and Salary is less than 50000, and also sort the resulting DataFrame by Name?
Answer: A
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Suppose you have a DataFrame df with columns Name, Age, Gender, and Salary. Which of the following code snippets will filter the DataFrame to include only rows where Age is more than 25 and Salary is less than 50000, and also sort the resulting DataFrame by Name?
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Question 3
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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 4
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You're fetching data from an API that returns data in JSON format. After fetching the data using Python's requests library, what should you do next to convert the returned data into a Python dictionary?
Answer: A
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You're fetching data from an API that returns data in JSON format. After fetching the data using Python's requests library, what should you do next to convert the returned data into a Python dictionary?
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Question 5
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What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?
Answer: D
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What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?