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ISQI CT-GenAI Exam Syllabus Topics:

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

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CT-GenAI Exam Lab Questions & Valid CT-GenAI Exam Papers

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q16-Q21):

NEW QUESTION # 16
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 # 17
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: B

Explanation:
A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.


NEW QUESTION # 18
A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

Answer: B

Explanation:
In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
"Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
"Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.


NEW QUESTION # 19
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

Answer: B

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 # 20
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: A

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 # 21
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