100% Pass 2026 CT-GenAI: Marvelous ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Study Center

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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q13-Q18):

NEW QUESTION # 13
What is a hallucination in LLM outputs?

Answer: B

Explanation:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.


NEW QUESTION # 14
A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

Answer: A

Explanation:
In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
"Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
"Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.


NEW QUESTION # 15
What is a primary compliance concern related to Shadow AI in organizational test environments?

Answer: C

Explanation:
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


NEW QUESTION # 16
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Answer: D

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 # 17
Who typically defines the system prompt in a testing workflow?

Answer: A

Explanation:
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.


NEW QUESTION # 18
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