CT-GenAI認定試験に関連する参考資料を提供できるサイトが多くあります。しかし、資料の品質が保証されることができません。それと同時に、あなたに試験に失敗すれば全額返金という保障を与えることもできません。普通の参考資料と比べて、JPTestKingのCT-GenAI問題集は最も利用に値するツールです。JPTestKingの指導を元にして、あなたは試験の準備を十分にすることができます。しかも、楽に試験に合格することができます。IT領域でより大きな進歩を望むなら、CT-GenAI認定試験を受験する必要があります。IT試験に順調に合格することを望むなら、JPTestKingのCT-GenAI問題集を使用する必要があります。
| Section | Weight | Objectives |
|---|---|---|
| Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies |
| Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
| Introduction to Generative AI for Software Testing | 15% | - Core concepts: Generative AI, LLMs, foundation models - Use cases across the testing lifecycle - Capabilities and limitations relevant to testing |
| LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness |
ISQIたぶん、CT-GenAI試験に合格するのが難しいと思うほど多くの受験者がいます。 しかし、今では、それについて心配する必要はありません。優れた試験資料を提供するからです。 当社JPTestKingのCT-GenAI試験教材は非常に有用であり、テストで高得点を獲得するのに役立ちます。 また、タイミングの機能と試験をシミュレートする機能が強化されるため、回答の速度を向上させ、テストの準備を完全に行うことができます。 CT-GenAI試験トレントは、試験に合格し、理想的な仕事を見つけるのに役立ちます。 CT-GenAI試験資料の内容についてご質問がある場合は、カスタマーサービスがオンラインで満足のいく回答を提供します。 製品を購入する前に、ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0ガイド急流の特徴と利点を次のように詳細に理解してください。
質問 # 14
What is a primary compliance concern related to Shadow AI in organizational test environments?
正解：C
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 # 15
A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?
正解：D
解説：
A/B testing, also known as split testing, is a systematic empirical method used to compare two versions of a prompt (Version A and Version B) to determine which one performs better based on predefined evaluation metrics. In the realm of LLMs, where outputs can be stochastic (probabilistic), A/B testing is essential for mitigating inconsistency. When a team encounters vague or varying results for a user story, simply modifying the prompt iteratively (Option B) may improve the result but does not provide a statistical or objective basis for why one version is superior. Byrunning A/B tests, testers can evaluate prompts against specific KPIs such as accuracy, relevance, format adherence, or the absence of hallucinations. This process involves sending the same input data through both prompt versions multiple times and scoring the outputs. The version that consistently yields the "stronger wording" or more precise testware is then selected as the production standard. This data-driven approach is a cornerstone of prompt engineering in professional environments, ensuring that the most effective linguistic structures are utilized to maximize the model's performance and reliability.
質問 # 16
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?
正解：D
解説：
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.
質問 # 17
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 # 18
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
正解：C
解説：
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.
質問 # 19
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今日の社会では、能力を高めるために証明書を取得することを優先する人がますます増えています。 ISQIまったく新しい観点から、JPTestKingのCT-GenAI学習資料は、CT-GenAI認定の取得を目指すほとんどのオフィスワーカーに役立つように設計されています。 当社のCT-GenAIテストガイドは、現代の人材開発に歩調を合わせ、すべての学習者を社会のニーズに適合させます。 ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0の最新の質問が、関連する知識の蓄積と能力強化のための最初の選択肢になることは間違いありません。
CT-GenAI技術問題: https://www.jptestking.com/CT-GenAI-exam.html