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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q29-Q34):

NEW QUESTION # 29
What are the three key phases in adopting GenAI in a test organization?

Answer: A

Explanation:
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


NEW QUESTION # 30
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 # 31
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 # 32
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Answer: D

Explanation:
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.


NEW QUESTION # 33
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

Answer: B

Explanation:
This scenario describes a form ofRequest Manipulation, specifically a type of "Prompt Injection" or
"Adversarial Prompting." In this attack vector, the user (or an external attacker) provides malicious or deceptive input-in this case, via an image in a multimodal LLM-to bypass the model's intended constraints or to steer its logic toward an unintended outcome. By crafting an image that tricks the LLM into seeing
"acceptance criteria" that aren't actually there, the attacker manipulates the model's request processing to generate false validation results. This is different fromData Poisoning(Option A), which involves corrupting the training data before the model is even built. It is also distinct fromData Exfiltration(Option B), which aims to steal data from the model. In a testing environment, request manipulation is a significant risk because it can lead to "Silent Failures," where the AI reports that tests have passed or requirements are met based on deceptive input, thereby compromising the integrity of the entire Quality Assurance process.


NEW QUESTION # 34
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