CT-GenAI Ausbildungsressourcen, CT-GenAI Exam

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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q33-Q38):

33. Frage
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

Antwort: B

Begründung:
ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
"requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.


34. Frage
In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

Antwort: A

Begründung:
Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
"ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.


35. Frage
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?

Antwort: C

Begründung:
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.


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

Antwort: A

Begründung:
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.


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

Antwort: B

Begründung:
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.


38. Frage
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