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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q10-Q15):

NEW QUESTION # 10
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Answer: A

Explanation:
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.


NEW QUESTION # 11
Which AI approach requires feature engineering and structured data preparation?

Answer: A

Explanation:
Classical Machine Learning(which includes algorithms like Random Forests, Support Vector Machines, and Linear Regression) is characterized by its reliance onFeature Engineering. This is the process where human experts manually select, extract, and transform raw data into a set of "features" or variables that the algorithm can process. For instance, in a classical ML model predicting software defects, a tester might have to manually define features like "lines of code changed" or "number of previous bugs." In contrast,Deep Learningand its subset,Generative AI(Options B and D), utilize "Representation Learning." This means the multi-layered neural networks automatically identify and extract the relevant features from raw, often unstructured data (like text or images) without explicit human instruction.Symbolic AI(Option A) is based on hard-coded logical rules rather than data-driven learning. Understanding this distinction is fundamental for testers, as it determines the level of data preparation required: Classical ML requires high human effort in data structuring, while GenAI requires high effort in prompt engineering and grounding.


NEW QUESTION # 12
Who typically defines the system prompt in a testing workflow?

Answer: A

Explanation:
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.


NEW QUESTION # 13
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 # 14
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: C

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 # 15
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