CT-GenAI Prüfungsfrage - CT-GenAI Prüfungsaufgaben

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q24-Q29):

24. Frage
What BEST protects sensitive test data at rest and in transit?

Antwort: C

Begründung:
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.


25. Frage
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Antwort: D

Begründung:
As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.


26. Frage
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

Antwort: D

Begründung:
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.


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

Antwort: C

Begründung:
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.


28. Frage
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

Antwort: B


29. Frage
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