ISQI CT-GenAI復習解答例 & CT-GenAI日本語サンプル

2026年Xhs1991の最新CT-GenAI PDFダンプおよびCT-GenAI試験エンジンの無料共有：https://drive.google.com/open?id=1xuRWJN8yO52LLqaYoFpli7nq7ayhEA4F

Xhs1991のCT-GenAI問題集はあなたを楽に試験の準備をやらせます。それに、もし最初で試験を受ける場合、試験のソフトウェアのバージョンを使用することができます。これは完全に実際の試験雰囲気とフォーマットをシミュレートするソフトウェアですから。このソフトで、あなたは事前に実際の試験を感じることができます。そうすれば、実際のCT-GenAI試験を受けるときに緊張をすることはないです。ですから、心のリラックスした状態で試験に出る問題を対応することができ、あなたの正常なレベルをプレイすることもできます。

ISQI CT-GenAI Exam Syllabus Topics:

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

>> ISQI CT-GenAI復習解答例 <<

CT-GenAI試験の準備方法｜更新するCT-GenAI復習解答例試験｜100％合格率のISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0日本語サンプル

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q16-Q21):

質問 # 16
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

正解：C

解説：
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.


質問 # 17
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 # 18
What are the three key phases in adopting GenAI in a test organization?

正解：B

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 # 19
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

正解：C

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 # 20
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 # 21
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CT-GenAI日本語サンプル: https://www.xhs1991.com/CT-GenAI.html

2026年Xhs1991の最新CT-GenAI PDFダンプおよびCT-GenAI試験エンジンの無料共有：https://drive.google.com/open?id=1xuRWJN8yO52LLqaYoFpli7nq7ayhEA4F