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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q30-Q35):

NEW QUESTION # 30
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

Answer: C

Explanation:
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.


NEW QUESTION # 31
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

Answer: D

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 # 32
What are the three key phases in adopting GenAI in a test organization?

Answer: B

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 # 33
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Answer: B

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 # 34
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Answer: D

Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.


NEW QUESTION # 35
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