CT-GenAI資料勉強 & CT-GenAI予想試験

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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q29-Q34):

質問 # 29
What is a primary compliance concern related to Shadow AI in organizational test environments?

正解：A

解説：
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


質問 # 30
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

正解：A

解説：
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.


質問 # 31
What are the three key phases in adopting GenAI in a test organization?

正解：C

解説：
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.


質問 # 32
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

正解：C

解説：
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.


質問 # 33
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

正解：B

解説：
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.


質問 # 34
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