Certified AI Red Teamer Practice Exam 2

Second full-length practice exam with different question set for certification preparation.

Assessment Overview

Second full-length practice exam with different question set for certification preparation. This assessment tests both theoretical understanding and practical application of the covered material.

Assessment Details:

• Questions: 15
• Time Limit: 45 minutes recommended
• Passing Score: 70%
• Difficulty: Intermediate

Instructions

1. Read each question carefully, including all answer options, before selecting your response
2. Some questions have multiple correct approaches — select the best answer
3. Scenario-based questions require you to consider the full context before answering
4. After completing the assessment, review the explanations for all questions — including those you answered correctly

Question 1

Question 2

Question 3

Question 4

Question 5

Question 6

Question 7

Question 8

Question 9

Question 10

Question 11

Question 12

Question 13

Question 14

Question 15

Scoring Guide

Study Resources

If you scored below the passing threshold, focus your review on:

• The specific topics where you answered incorrectly
• The hands-on labs that correspond to your weakest areas
• The reference materials for frameworks and tools mentioned in the questions
• CVE-2024-21513 — LangChain experimental: arbitrary code execution

Detailed Assessment Content

Section Review Material

Before attempting this assessment, ensure you have reviewed all relevant curriculum material. The questions in this assessment cover both theoretical concepts and practical application of the material.

Key areas of focus for this assessment:

1. Conceptual understanding: You should be able to explain the fundamental mechanisms behind the attack and defense techniques covered, not just recognize them by name.

2. Practical application: Many questions present scenarios that require you to determine the best approach given specific constraints and objectives.

3. Tool proficiency: Questions may reference specific tools and their capabilities. Ensure you have hands-on experience with the tools covered in the labs.

4. Framework knowledge: Governance and compliance questions reference specific frameworks (OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF, EU AI Act). Know the key provisions and how they apply to AI security.

Practice Scenarios

Consider the following scenarios as additional preparation:

Scenario 1: You are conducting a red team assessment of a customer-facing chatbot. The chatbot has access to a CRM system through function calling and can look up customer records, create tickets, and send emails. What is your initial approach?

Consider: What information do you need to gather first? What are the highest-risk capabilities to test? How would you structure your testing to maximize coverage while staying within scope?

Scenario 2: During testing, you discover that the chatbot's system prompt can be extracted through a simple instruction override. The system prompt contains API endpoint URLs and authentication patterns. What do you do?

Consider: What is the immediate risk? Should you continue testing or escalate immediately? How would you document this finding?

Scenario 3: You are evaluating a RAG system that ingests customer documents. You have the ability to upload documents as a normal user. What attack vectors should you explore?

Consider: Document-based injection, metadata manipulation, embedding space poisoning, and the interaction between document processing and the LLM.

# Framework for structured scenario analysis
class ScenarioAnalysis:
    """Structured analysis framework for assessment scenarios."""
 
    def __init__(self, scenario_description: str):
        self.scenario = scenario_description
        self.threats = []
        self.vectors = []
        self.priorities = []
 
    def identify_threats(self) -> list:
        """Identify threats relevant to the scenario."""
        # Map scenario to OWASP LLM Top 10 categories
        owasp_mapping = {
            "function_calling": "LLM06 - Excessive Agency",
            "system_prompt": "LLM01 - Prompt Injection",
            "rag_documents": "LLM01 - Prompt Injection (Indirect)",
            "user_data": "LLM02 - Insecure Output Handling",
            "training_data": "LLM03 - Training Data Poisoning",
        }
        return self.threats
 
    def prioritize_testing(self) -> list:
        """Prioritize testing vectors by risk."""
        # Risk = Likelihood × Impact
        return sorted(self.vectors, key=lambda v: v["risk_score"], reverse=True)
 
    def generate_test_plan(self) -> dict:
        """Generate a structured test plan for the scenario."""
        return {
            "scope": self.scenario,
            "threats": self.threats,
            "test_cases": [
                {
                    "vector": v["name"],
                    "technique": v["technique"],
                    "expected_result": v["expected"],
                    "priority": v["risk_score"],
                }
                for v in self.prioritize_testing()
            ],
        }

Additional Knowledge Checks

References and Further Reading

• CVE-2024-21513 — LangChain experimental: arbitrary code execution
• JailbreakBench — github.com/JailbreakBench/jailbreakbench
• OWASP LLM Top 10 2025 — LLM01 (Prompt Injection)