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ISQI CT-GenAI Exam Syllabus Topics:

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

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The Platform ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Exam credential makes it simple to renew your skills and knowledge to keep up with the latest trends. The Platform ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI exam certification is a worthwhile, internationally accepted industry credential. You can become a recognized specialist in addition to learning new technological needs and honing your abilities.

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q11-Q16):

NEW QUESTION # 11
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 # 12
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: B

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 # 13
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

Answer: B

Explanation:
ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
"requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.


NEW QUESTION # 14
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

Answer: D

Explanation:
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.


NEW QUESTION # 15
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 # 16
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