Free Python Institute PCED-30-01 Exam Questions

Become Python Institute Certified with updated PCED-30-01 exam questions and correct answers

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Total 382 Questions | Updated On: Aug 14, 2025

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Question 1

You notice that the transformation phase of the ETL process is extremely slow. You suspect that the issue lies in the inefficient handling of a large DataFrame in Pandas. Which of the following should be your first step to troubleshoot?

A : Break the data into smaller chunks and process them sequentially.

B : Switch to a different data processing library like Dask or PySpark.

C : Upgrade the hardware to speed up the process.

D : Review and optimize the Pandas code for performance.

Answer: D

Question 2

When dealing with a categorical feature that has high cardinality in a dataset, which of the following approaches is most advisable for one-hot encoding?

A : Use label encoding instead of one-hot encoding

B : Group less frequent categories into a single "Other" category before one-hot encoding

C : Remove the feature from the dataset

D : One-hot encode all unique values in the feature

Answer: B

Question 3

You need to collect data from an Excel file with multiple sheets. One of the sheets is named "Sales" and another is named "Inventory". You want to load the "Sales" sheet into a DataFrame named df_sales and the "Inventory" sheet into a DataFrame named df_inventory. Which of the following code snippets accomplishes this?

A : xls = pd.Excel('file.xlsx')df_sales = xls.parse('Sales')df_inventory = xls.parse('Inventory')

B : df_sales = pd.read_excel('file.xlsx', sheet_name='Sales')df_inventory = pd.read_excel('file.xlsx', sheet_name='Inventory')

C : df_sales = pd.read_csv('file.xlsx', sheet_name='Sales')df_inventory = pd.read_csv('file.xlsx', sheet_name='Inventory')

D : df_sales = pd.read_excel('file.xlsx').parse('Sales')df_inventory = pd.read_excel('file.xlsx').parse('Inventory')

Answer: B

Question 4

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?

A : data_dict = response.json()

B : import json data_dict = json.load(response.text)

C : import json data_dict = json.loads(response.content)

D : data_dict = eval(response.text)

Answer: A

Question 5

You are working with a dataset that has a mix of numerical and textual data. Your job is to synthesize this information into a more digestible form. What technique could you use to handle both types of data effectively for downstream analysis?

A : Run a Principal Component Analysis (PCA) on the entire dataset.

B : Implement a Decision Tree algorithm to identify the most important features.

C : Use k-Nearest Neighbors (k-NN) to classify data points.

D : Utilize Natural Language Processing (NLP) for textual data and Min-Max scaling for numerical data.

Answer: D

Page: 1 / 77

Total 382 Questions | Updated On: Aug 14, 2025

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