P.S.GoShiken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=15UDqVLTC44EmK29OeIRCllaPRETKHDAo
当社のCT-GenAI学習ツールは、すべての受験者に高い合格率のCT-GenAI学習教材を提供するだけでなく、優れたサービスを提供します。当社または当社の製品について質問または疑問がある場合は、当社に連絡して解決してください。 CT-GenAI学習ガイドサービスの思慮深さは圧倒的です。私たちが行うことは、CT-GenAI実践教材の成功に貢献します。したがって、CT-GenAI実践教材は、ユーザーが今後の求人検索でより多くの利点を得ることができるため、ユーザーは激しい競争で際立って最高の成績を収めることができます。
| Section | Objectives |
|---|---|
| Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
| Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
| Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Prompt Engineering for Testing | - Prompt design techniques
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| Application of GenAI in Software Testing | - Practical use in testing workflows
|
GoShikenのISQIのCT-GenAI試験資料は同じシラバスに従って研究されたのです。それに、資料もずっとアップグレードしていますから、実際の試験問題とよく似ています。GoShikenの試験合格率も非常に高いことは否定することができない事実です。GoShikenのISQIのCT-GenAI試験トレーニング資料の値段は手頃で、IT認証の受験生のみなさんによく適用します。
質問 # 33
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?
正解：D
解説：
LLMOps(Large Language Model Operations) is the set of practices used to manage the lifecycle of LLMs in production. When an organization integrates an AI chatbot into its test processes, the primary operational concern ismaintaining data privacy and minimizing security risks, especially if using third-party APIs.
Unlike traditional software, LLMs are "black boxes" that process every piece of data sent to them. A core LLMOps responsibility is ensuring that any "Prompt Data" (code, requirements, or logs) is not used by the provider to train their public models and that the communication channels are fully secured. While scalability (Option A) and latency (Option C) are important technical metrics, they are secondary to the catastrophic legal and reputational risk of a data breach. LLMOps in a testing context involves implementing data masking tools, monitoring for "Prompt Injection" attacks, and managing the "Grounding" data in vector databases to ensure it remains current and protected. This ensures the AI remains a safe and reliable asset within the enterprise testing ecosystem, rather than a liability for the organization's intellectual property.
質問 # 34
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
正解：B
解説：
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.
質問 # 35
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
正解：B
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 # 36
In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.
正解：A
解説：
Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
"ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.
質問 # 37
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：B
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 # 38
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CT-GenAI日本語版問題解説: https://www.goshiken.com/ISQI/CT-GenAI-mondaishu.html
ちなみに、GoShiken CT-GenAIの一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=15UDqVLTC44EmK29OeIRCllaPRETKHDAo