Free PDF Quiz 2026 ISQI High Pass-Rate CT-GenAI: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Exam Cram Review

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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Security testing tools
  • 2. Simulation and monitoring tools
  • 3. Model evaluation tools
  • 4. Prompt testing tools
- Testing Tools Overview
  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs
Topic 2: Fundamentals of Generative AI20%- AI Terminology
  • 1. Transformer architecture
  • 2. Deep Learning
  • 3. Artificial Intelligence (AI)
  • 4. Generative AI (GenAI)
  • 5. Tokens and prompts
  • 6. Large Language Models (LLMs)
  • 7. Machine Learning (ML)
- Generative AI Concepts
  • 1. Emergent capabilities and limitations
  • 2. Hallucinations
  • 3. Alignment and guardrails
  • 4. Model types (Base, Instruction-tuned, RAG)
  • 5. Training data and context windows
  • 6. AI model behavior
- AI Development Lifecycle
  • 1. Deployment and monitoring
  • 2. Model training and fine-tuning
  • 3. Data collection, preparation, and curation
  • 4. Evaluation
Topic 3: Testing Activities for Generative AI30%- Prompt-Based Testing
  • 1. Test data creation with GenAI
  • 2. Prompt engineering basics
  • 3. Test case design using prompts
- Model and Output Evaluation
  • 1. Checkpoint testing
  • 2. Automated evaluation methods
  • 3. Output correctness and quality assessment
  • 4. Metamorphic testing
  • 5. Human evaluation methods
- Requirements-Based Testing
  • 1. Functional requirements for AI-based systems
  • 2. Non-functional requirements for AI-based systems
  • 3. AI-related quality requirements
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components
Topic 4: Risks and Testing Challenges for Generative AI30%- Testing Challenges for Generative AI
  • 1. Test oracle problem
  • 2. Complexity of the AI component
  • 3. Ethical testing concerns
  • 4. Subjectivity of quality assessment
  • 5. Regulatory and compliance considerations
  • 6. Non-deterministic output behavior
  • 7. Coverage challenges
- Quality Risks Specific to Generative AI
  • 1. Inappropriate output for the context
  • 2. Offensive, harmful, or biased content
  • 3. Input sensitivity (prompt brittleness)
  • 4. Incorrect or fabricated outputs (hallucinations)
  • 5. Inconsistent responses across runs
  • 6. Dependency on external components

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q18-Q23):

NEW QUESTION # 18
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Answer: B

Explanation:
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.


NEW QUESTION # 19
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Answer: B

Explanation:
As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.


NEW QUESTION # 20
Which statement about fine-tuning for test tasks is INCORRECT?

Answer: A

Explanation:
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.


NEW QUESTION # 21
What defines a prompt pattern in the context of structured GenAI capability building?

Answer: D

Explanation:
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.


NEW QUESTION # 22
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Answer: B

Explanation:
Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
"project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.


NEW QUESTION # 23
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