CT-GenAI試験トレントは3つのバージョンをブーストし、PDFバージョン、PCバージョン、APPオンラインバージョンが含まれます。 3つのバージョンは、ISQIそれぞれの強度と使用方法を高めます。たとえば、PCバージョンのCT-GenAI試験トレントは、インストールソフトウェアアプリケーションをブーストし、実際のCT-GenAI試験をシミュレートし、MSオペレーティングシステムをサポートし、練習用に2つのモードをブーストし、いつでもオフラインで練習できます。コンピューター、携帯電話、ラップトップでAPPオンラインバージョンのCT-GenAIガイドトレントを学習でき、最も便利な学習方法を選択できます。
| 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: 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 |
| Topic 3: 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 4: Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
| 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 |
CT-GenAI学習教材を購入すると、CT-GenAIテストにスムーズかつ簡単に合格します。プロの専門家チームを強化してCT-GenAIトレーニング資料を熱心に編成および編集し、販売前後のサービス、24時間のオンラインカスタマーサービス、払い戻しサービスなどの素晴らしいサービスを提供します。 CT-GenAIの実際のクイズでは、3つのバージョンとさまざまな機能が強化され、包括的かつ効率的に学習できます。学習教材の学習は時間と労力をほとんど必要とせず、頻繁に更新されます。質問：ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0の詳細については、次のように製品の紹介をご覧ください。
質問 # 32
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
正解：D
解説：
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.
質問 # 33
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 # 34
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?
正解：B
解説：
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.
質問 # 35
Which concept refers to breaking text into smaller units for processing by LLMs?
正解：B
解説：
Tokenizationis the foundational process by which an LLM breaks down raw text into smaller, manageable units called "tokens." These tokens can represent individual words, parts of words (sub-words), or even punctuation marks. This is a critical step because LLMs do not "read" words like humans do; they process numerical representations of these tokens. The way text is tokenized directly impacts the model's efficiency and its ability to understand complex technical terminology used in software testing. For example, a rare technical term might be broken into several sub-word tokens. This process is closely linked to theContext Window(Option C), which is the maximum number of tokens a model can "remember" or process at one time. WhileEmbeddings(Option B) are the numerical vectors that represent the meaning of these tokens, and theTransformer(Option A) is the underlying architecture that processes them, tokenization is the specific mechanism for initial text decomposition. Understanding tokenization is vital for testers when managing long requirement documents to ensure they do not exceed the model's limits.
質問 # 36
What is a hallucination in LLM outputs?
正解：B
解説：
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.
質問 # 37
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人によって目標が違いますが、あなたにISQI CT-GenAI試験に順調に合格できるのは我々の共同の目標です。この目標の達成はあなたがIT技術領域へ行く更なる発展の一歩ですけど、我々社JPTestKing存在するこそすべての意義です。だから、我々社は力の限りで弊社のISQI CT-GenAI試験資料を改善し、改革の変更に応じて更新します。あなたはいつまでも最新版の問題集を使用できるために、ご購入の一年間で無料の更新を提供します。
CT-GenAI日本語版参考書: https://www.jptestking.com/CT-GenAI-exam.html