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ISQI CT-GenAI Exam Syllabus Topics:

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

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

Answer: B

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 # 13
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

Answer: D

Explanation:
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.


NEW QUESTION # 14
What defines a prompt pattern in the context of structured GenAI capability building?

Answer: C

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 # 15
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Answer: D

Explanation:
As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.


NEW QUESTION # 16
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