PCED-30-01 Exam: Absolutely Free Online Practice

Question 1
- 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?
  
  A : df[(df['Age'] > 25) & (df['Salary'] < 50000)].sort_values('Name')
  
  B : df.query('Age > 25 and Salary < 50000').sort('Name')
  
  C : df.sort_values('Name').where(df['Age'] > 25 & df['Salary'] < 50000)
  
  D : df.filter('Age' > 25 & 'Salary' < 50000).sort_values(by='Name')
  
  Answer: A
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
- Your dataset includes several categorical variables. What is the most efficient way to convert these variables into a format that can be fed into a machine learning model?
  
  A : Use label encoding indiscriminately
  
  B : Apply one-hot encoding selectively
  
  C : Convert all categories to their alphabetical rank
  
  D : Use frequency encoding for all categorical variables
  
  Answer: B
Question 4
- You are a data analyst who needs to collect large volumes of data from a RESTful API that has pagination implemented. Which Python code snippet would be the most effective way to gather all the available data?
  
  A : import requests
  
  page = 1
  while True:
  response = requests.get(f"https://api.example.com/data?page={page}")
  if response.status_code != 200:
  break
  data = response.json()
  # Process data
  page += 1
  
  B : import requests
  
  response = requests.get("https://api.example.com/data")
  data = response.json()
  
  C : import requests
  
  pages = [1, 2, 3, 4, 5]
  for page in pages:
  response = requests.get(f"https://api.example.com/data?page={page}")
  data = response.json()
  # Process data
  
  D : import requests
  
  for page in range(1, 100):
  response = requests.get(f"https://api.example.com/data?page={page}")
  data = response.json()
  # Process data
  
  Answer: A
Question 5
- In a given dataset, you have a categorical feature "State" that includes the 50 U.S. states. You decide to one-hot encode this feature. What potential issue should you be cautious about?
  
  A : Introducing multicollinearity
  
  B : Reducing the dimensionality of the dataset
  
  C : Creating an imbalanced dataset
  
  D : Inducing underfitting in the model
  
  Answer: A

Free Python Institute PCED-30-01 Exam Questions

Absolute Free PCED-30-01 Exam Practice for Comprehensive Preparation