Free Practice Python Institute PCED Exam Questions 2026

Stay ahead with 100% Free Certified Entry-Level Data Analyst with Python PCED Dumps Practice Questions

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Total 382 Questions | Updated On: Mar 07, 2026

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Question 1

You are given a JSON file containing social data about users' interactions on a website. The JSON object includes an array named likes that should contain integers representing the number of likes each post has received. Which of the following Python code snippets would be most appropriate to validate that all elements in the likes array are indeed integers?

A : import json data = json.load(open('data.json')) if len(data['likes']) > 0: print("Data is valid")else: print("Data is invalid")

B : import json data = json.load(open('data.json')) if all(isinstance(x, int) for x in data['likes']): print("Data is valid")else: print("Data is invalid")

C : import json data = json.loads('data.json') if isinstance(data['likes'], list): print("Data is valid")else: print("Data is invalid")

D : import json data = json.load(open('data.json')) if 'likes' in data.keys(): print("Data is valid")else: print("Data is invalid")

Answer: B

Question 2

What is the most appropriate technique to handle missing values for a categorical column in a dataset before fitting it to a model?

A : Remove Rows with Missing Values

B : Mean Imputation

C : Linear Regression Imputation

D : Mode Imputation

Answer: D

Question 3

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?

A : Increase the model's learning rate.

B : Add more features to the model.

C : Remove all features and retrain the model.

D : Regularize the model by introducing L1 or L2 penalties.

Answer: D

Question 4

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 5

Suppose you are working on a classification problem with an imbalanced dataset. Which of the following techniques would not be effective for addressing the issue?

A : Using accuracy as the evaluation metric

B : Using the Synthetic Minority Over-sampling Technique (SMOTE)

C : Using different evaluation metrics such as F1-score.

D : Under-sampling the majority class

Answer: A

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Total 382 Questions | Updated On: Mar 07, 2026

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