Free Python Institute PCED-30-01 Exam Questions
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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 08, 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've built a logistic regression model to predict whether a loan applicant will default on a loan (Default or No Default). After training, the model's AUC-ROC score is 0.95 on the training set but only 0.60 on the test set. What is most likely the reason for this discrepancy?
Answer: A
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You've built a logistic regression model to predict whether a loan applicant will default on a loan (Default or No Default). After training, the model's AUC-ROC score is 0.95 on the training set but only 0.60 on the test set. What is most likely the reason for this discrepancy?
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Question 2
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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 3
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When using Python's requests library to collect data from a website through HTTP requests, which status code indicates that the requested resource has been moved permanently?
Answer: D
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When using Python's requests library to collect data from a website through HTTP requests, which status code indicates that the requested resource has been moved permanently?
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Question 4
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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 5
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In a large dataset, you notice that one column has some entries recorded in uppercase and others in lowercase. This inconsistency could potentially cause errors in downstream analyses. What is the best way to handle this issue?
Answer: C
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In a large dataset, you notice that one column has some entries recorded in uppercase and others in lowercase. This inconsistency could potentially cause errors in downstream analyses. What is the best way to handle this issue?