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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q39-Q44):

NEW QUESTION # 39
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

Answer: A

Explanation:
LLMOps(Large Language Model Operations) is the set of practices used to manage the lifecycle of LLMs in production. When an organization integrates an AI chatbot into its test processes, the primary operational concern ismaintaining data privacy and minimizing security risks, especially if using third-party APIs.
Unlike traditional software, LLMs are "black boxes" that process every piece of data sent to them. A core LLMOps responsibility is ensuring that any "Prompt Data" (code, requirements, or logs) is not used by the provider to train their public models and that the communication channels are fully secured. While scalability (Option A) and latency (Option C) are important technical metrics, they are secondary to the catastrophic legal and reputational risk of a data breach. LLMOps in a testing context involves implementing data masking tools, monitoring for "Prompt Injection" attacks, and managing the "Grounding" data in vector databases to ensure it remains current and protected. This ensures the AI remains a safe and reliable asset within the enterprise testing ecosystem, rather than a liability for the organization's intellectual property.


NEW QUESTION # 40
What BEST protects sensitive test data at rest and in transit?

Answer: D

Explanation:
Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.


NEW QUESTION # 41
Which AI approach requires feature engineering and structured data preparation?

Answer: B

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 # 42
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Answer: D

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 # 43
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: C

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 # 44
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