CT-GenAI Simulationsfragen - CT-GenAI Fragen Und Antworten

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ISQI CT-GenAI Exam Syllabus Topics:

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

>> CT-GenAI Simulationsfragen <<

CT-GenAI aktueller Test, Test VCE-Dumps für ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q23-Q28):

23. Frage
What are the three key phases in adopting GenAI in a test organization?

Antwort: B

Begründung:
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


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

Antwort: A

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.


25. Frage
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

Antwort: A

Begründung:
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.


26. Frage
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Antwort: B


27. Frage
What is a hallucination in LLM outputs?

Antwort: C

Begründung:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.


28. Frage
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CT-GenAI Fragen Und Antworten: https://www.deutschpruefung.com/CT-GenAI-deutsch-pruefungsfragen.html

P.S. Kostenlose 2026 ISQI CT-GenAI Prüfungsfragen sind auf Google Drive freigegeben von DeutschPrüfung verfügbar: https://drive.google.com/open?id=1-7eyaThFHu-MajzNqFIm-2s-_O2X6ZBO