PCED Exam: Absolutely Free Online Practice

Question 1
- 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 2
- 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 3
- You are working on a project that requires real-time data collection from a WebSocket API. Which of the following Python libraries would be most appropriate for this task?
  
  A : csv
  
  B : websocket-client
  
  C : urllib
  
  D : scrapy
  
  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
- 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

Free Python Institute PCED Exam Questions

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