PCED Exam: Absolutely Free Online Practice

Question 1
- Given a DataFrame df:
  df = pd.DataFrame({
  'A': [1, 2, 3],
  'B': [4, 5, 6]
  })
  You want to square each individual element in the DataFrame. Which of the following code will accomplish this?
  A : df.applymap(lambda x: x**2)
  
  B : df['A'].apply(lambda x: x**2)
  
  C : df.map(lambda x: x**2)
  
  D : df.apply(lambda x: x**2, axis=1)
  
  Answer: A
Question 2
- 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 3
- 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 4
- While evaluating a logistic regression model using a confusion matrix, you notice that the True Positive rate is significantly higher than the True Negative rate. What does this observation imply?
  
  A : The model is better at identifying the positive class than the negative class
  
  B : The model performs equally well for both classes
  
  C : The model has a high level of bias
  
  D : The model is overfitting
  
  Answer: A
Question 5
- When would you typically use the iloc attribute as opposed to the loc attribute in a pandas DataFrame?
  
  A : When you want to apply a lambda function to each row
  
  B : When you want to drop missing values from the DataFrame
  
  C : When you want to access rows and columns by label
  
  D : When you want to access rows and columns by integer-location
  
  Answer: D

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