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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q24-Q29):

NEW QUESTION # 24
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 # 25
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Answer: A

Explanation:
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.


NEW QUESTION # 26
Which option BEST differentiates the three prompting techniques?

Answer: B

Explanation:
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.


NEW QUESTION # 27
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

Answer: B

Explanation:
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.


NEW QUESTION # 28
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 # 29
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