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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q35-Q40):

NEW QUESTION # 35
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

Answer: D

Explanation:
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.


NEW QUESTION # 36
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

Answer: D

Explanation:
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.


NEW QUESTION # 37
Which statement BEST contrasts interaction style and scope?

Answer: D

Explanation:
It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
"LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.


NEW QUESTION # 38
What defines a prompt pattern in the context of structured GenAI capability building?

Answer: B

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 # 39
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

Answer: D

Explanation:
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.


NEW QUESTION # 40
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