Free PCED Exam Questions - Easiest Way for Success

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Total 382 Questions | Updated On: Sep 13, 2024

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Question 1

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 2

You are working with a large dataset containing multi-dimensional features related to customer behavior. The dataset is too large and complex to analyze easily. What technique could be most effective for reducing the dataset's dimensionality while retaining most of its original variance?

A : Principal Component Analysis (PCA)

B : One-hot encoding

C : Linear Discriminant Analysis (LDA)

D : Max-Min Scaling

Answer: A

Question 3

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?

A : df['A'] = df['A'].fillna(df['C'].mean())

B : df['A'].fillna(df['A'].mean(), inplace=True)

C : df.fillna({'A': df['A'].mean()})

D : df['A'].apply(lambda x: x.fillna(x.mean()))

Answer: B

Question 4

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 5

You have a dataset with 15 features and you want to visualize it on a 2D plot for exploratory data analysis. What would be the first step in using PCA (Principal Component Analysis) to achieve this?

A : Perform clustering on the dataset

B : Apply a linear regression model to reduce dimensions

C : Use a scatter plot to visualize all the features

D : Standardize the dataset

Answer: D

Page: 1 / 77

Total 382 Questions | Updated On: Sep 13, 2024

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