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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q25-Q30):

NEW QUESTION # 25
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

Answer: D

Explanation:
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.


NEW QUESTION # 26
Which of the following is NOT a valid form of LLM-driven test data generation?

Answer: A

Explanation:
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.


NEW QUESTION # 27
Which statement BEST describes vision-language models (VLMs)?

Answer: D

Explanation:
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.


NEW QUESTION # 28
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 # 29
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Answer: D

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 # 30
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