Free Dumps for any Professional Exam Certification 🕸️

This repository contains a Python script designed to scrape exam question links from the Exam Topics website based on a specific provider and exam code. The script is optimized to run in Google Colab and uses parallel requests for efficient data fetching.

Fetches 2.5 pages per second 📄⌛

Features ✨

• Scrapes exam question links from Exam Topics for a specified provider.
• Filters the links based on a given exam code.
• Uses multithreading to speed up the scraping process.
• Groups and saves the exam question links in a structured text file.

Setup Instructions 🚀

1. Open in Google Colab

To get started, open this script in Google Colab using the following link:

2. Install Required Libraries

Make sure to install the necessary Python libraries before running the script:

!pip install requests beautifulsoup4 tqdm

3. Run the Script

Get the code from main.ipynb or main.py and run it in a Colab cell.

Code Explanation 🧩

The script is divided into several parts, each with a specific purpose:

Import Libraries:

import requests
from bs4 import BeautifulSoup
import re
from concurrent.futures import ThreadPoolExecutor, as_completed
from tqdm import tqdm

• requests: For making HTTP requests.
• BeautifulSoup: For parsing HTML content.
• re: For regular expression operations.
• ThreadPoolExecutor: For parallel execution of tasks.
• tqdm: For displaying progress bars.

Scraper Class:

Initialization

class Scraper:
    def __init__(self, provider):
        self.session = requests.Session()
        self.provider = provider.lower()
        self.base_url = f"https://www.examtopics.com/discussions/{self.provider}/"

• Initializes a session for making requests.
• Sets the base URL based on the provided exam provider.

Get Number of Pages

    def get_num_pages(self):
        try:
            response = self.session.get(f"{self.base_url}")
            soup = BeautifulSoup(response.content, "html.parser")
            return int(soup.find("span", {"class": "discussion-list-page-indicator"}).find_all("strong")[1].text.strip())
        except Exception as e:
            print(f"Error fetching page count: {e}")
            return 0

• Fetches and parses the number of pages available for the provider.

Fetch Page Links

    def fetch_page_links(self, page, search_string):
        try:
            response = self.session.get(f"{self.base_url}{page}/")
            soup = BeautifulSoup(response.content, "html.parser")
            discussions = soup.find_all("a", {"class": "discussion-link"})
            links = [
                discussion["href"].replace("/discussions", "https://www.examtopics.com/discussions", 1)
                for discussion in discussions if search_string in discussion.text
            ]
            return links
        except Exception as e:
            print(f"\nError on page {page}: {e}")
            return []

• Fetches and filters exam question links from a specific page.

Get Exam Question Links

    def get_discussion_links(self, num_pages, search_string):
        links = []
        with ThreadPoolExecutor(max_workers=10) as executor:
            futures = [executor.submit(self.fetch_page_links, page, search_string) for page in range(1, num_pages + 1)]
            with tqdm(total=num_pages, desc="Fetching Links", unit="page") as pbar:
                for future in as_completed(futures):
                    page_links = future.result()
                    links.extend(page_links)
                    pbar.update(1)
        return links

• Uses parallel requests to fetch exam question links from multiple pages simultaneously.

Utility Functions

Extract Topic and Question

def extract_topic_question(link):
    match = re.search(r'topic-(\d+)-question-(\d+)', link)
    return (int(match.group(1)), int(match.group(2))) if match else (None, None)

• Extracts topic and question numbers from the exam question link.

Write Grouped Links to File

def write_grouped_links_to_file(filename, links):
    grouped_links = {}
    for link in sorted(links, key=extract_topic_question):
        topic, question = extract_topic_question(link)
        grouped_links.setdefault(topic, []).append(link)

    with open(filename, 'w') as f:
        for topic, links in grouped_links.items():
            f.write(f'Topic {topic}:\n')
            for link in links:
                f.write(f' - {link}\n')
            print(f"Topic {topic} links added to file.")

• Groups and writes the exam question links to a file based on their topic.

Main Function:

def main():
    provider = input("Enter provider name: ")
    scraper = Scraper(provider)
    num_pages = scraper.get_num_pages()
    print("Total Pages:", num_pages)
    if num_pages > 0:
        search_string = input("Enter exam code or enter QUIT to exit: ").upper()
        if search_string != 'QUIT':
            links = scraper.get_discussion_links(num_pages, search_string)
            filename = f'{search_string} dumps.txt'
            print(f"\nYour file will be named {filename}")
            write_grouped_links_to_file(filename, links)
            print("File generation complete.")
        else:
            return
    else:
        print("No pages found for the provider.")

if __name__ == "__main__":
    main()

• Prompts the user for the provider and exam code (put the correct exam code).
• Fetches and processes the exam question links.
• Saves the links to a text file.

License 📄

This project is licensed under the MIT License.