GIAC GPYC Exam Details & Actual Exam Questions

  • Exam Code/Number: GPYC
  • Exam Name/Title: GIAC Python Coder (GPYC)
  • Certification Provider: GIAC
  • Corresponding Certification: GIAC Security Certification
  • Exam Questions: 75
  • Updated On: Sep,24 2026
  • Certification Level: Practitioner

GIAC Python Coder (GPYC) Exam Questions

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GIAC GPYC Exam Overview:

Certification Vendor:GIAC (Global Information Assurance Certification)
Exam Name:GIAC Python Coder (GPYC)
Exam Number:GPYC
Real Exam Qty:75
Exam Price:$1,999 USD
Certificate Validity Period:4 years
Passing Score:67%
Exam Format:Proctored exam, Scenario-based questions, Multiple choice
Exam Duration:120 minutes
Available Languages:English
Recommended Training:GIAC GPYC Resources
SANS SEC573: Automating Information Security with Python
Exam Registration:Pearson VUE Scheduling
ProctorU Remote Exam
GIAC Official Registration
Sample Questions:GIAC GPYC Sample Questions
Exam Way:Web-based proctored exam: Remote via ProctorU or onsite at Pearson VUE centers
Pre Condition:No mandatory prerequisites; recommended basic Python knowledge and cybersecurity familiarity
Official Syllabus URL:https://www.giac.org/certifications/python-coder-gpyc/

GIAC GPYC Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Web & Database Interaction20%- Website Interaction
  • 1. HTTP requests and responses
    • 2. Cookies and session handling
      • 3. API integration
        - Database Operations
        • 1. Secure data handling
          • 2. Query execution
            • 3. Database connectivity
              Topic 2: Network & Packet Analysis20%- Network Programming
              • 1. TCP/UDP socket communication
                • 2. Network interface handling
                  - Packet Analysis with Scapy
                  • 1. Create/read/modify traffic captures
                    • 2. Protocol analysis
                      • 3. Security-focused packet inspection
                        Topic 3: Python Language Essentials20%- Control Structures & Iteration
                        • 1. Conditionals and loops
                          • 2. Exception handling
                            • 3. Error management
                              - Functions, Classes & OOP
                              • 1. Function definition and parameters
                                • 2. Type hints and docstrings
                                  • 3. Object-oriented programming
                                    - Python Basics
                                    • 1. Program creation, execution and debugging
                                      • 2. Strings, bytes, Unicode handling
                                        • 3. Input/output handling
                                          • 4. Data types and structures
                                            Topic 4: Security & Advanced Applications20%- AI & Automation
                                            • 1. Tool automation
                                              • 2. LLM API integration
                                                • 3. Code validation and security
                                                  - Executable Creation
                                                  • 1. Security considerations
                                                    • 2. Convert Python to standalone executables
                                                      Topic 5: Data Analysis & Processing20%- Data Structures & Manipulation
                                                      • 1. Lists, dictionaries, sets, tuples
                                                        • 2. Binary parsing
                                                          • 3. JSON and structured data
                                                            - Log & File Analysis
                                                            • 1. Structured data preparation
                                                              • 2. Common file formats
                                                                • 3. AI-assisted data workflows
                                                                  - Regular Expressions
                                                                  • 1. Pattern matching
                                                                    • 2. Log/text data extraction
                                                                      • 3. Named capture groups


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