P.S.Pass4TestがGoogle Driveで共有している無料の2026 ISQI CT-GenAIダンプ：https://drive.google.com/open?id=156B_0Y95yCA3Np6fjbys2qJZgXV-0JLZ
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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Topic 2: Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap |
| Topic 3: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts |
| Topic 4: Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models - Use cases across the testing lifecycle |
| Topic 5: Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies |
ISQIさまざまな種類の候補者がCT-GenAI認定を取得する方法を見つけるために、多くの研究が行われています。 シラバスの変更および理論と実践の最新の進展に応じて、CT-GenAIテストトレントを修正および更新します。 CT-GenAI認定トレーニングは、厳密な分析による近年のテストと業界動向に基づいています。 したがって、お客様のISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0のために、より多くの選択肢が用意されています。試験のためにCT-GenAI試験問題を選択することをお勧めします。
質問 # 24
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
正解：A
質問 # 25
Who typically defines the system prompt in a testing workflow?
正解：A
解説：
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.
質問 # 26
Which of the following is NOT a valid form of LLM-driven test data generation?
正解：B
解説：
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.
質問 # 27
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 # 28
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?
正解：B
解説：
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.
質問 # 29
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学習への関心を高めるには学習者に学習のための良い鍵を与えることが必要であり、これは学習者の内部要因の積極的な発達を促進することです。 CT-GenAI質問トレントの最大の機能は、お客様が優れた学習習慣を身に付け、学習への関心を高め、簡単に試験に合格し、CT-GenAI認定を取得できるようにすることです。候補者のために高品質の製品を生産するために、当社のすべての労働者が協力しています。私たちのCT-GenAI試験トレントはあなたの将来にとって非常に役立つと信じています。
CT-GenAI資格専門知識: https://www.pass4test.jp/CT-GenAI.html
2026年Pass4Test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=156B_0Y95yCA3Np6fjbys2qJZgXV-0JLZ