さらに、MogiExam CT-GenAIダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1Yoblzvh682FP88RkdRB4BMTJr7HwqlWy
私たちMogiExamは現在、競争の激しい世界に住んでいます。 CT-GenAI認定を取得するなど、ソフトパワーを改善する以外に選択肢はありません。 CT-GenAIトレントが試験に合格し、履歴書を強調することで職場で成功を収めることができます。 CT-GenAI試験に合格して認定資格を取得したい場合は、CT-GenAIガイドの質問があなたの理想的な選択であることを確認できます。当社は、CT-GenAI試験問題に関する専門チーム、高品質のサービス、リーズナブルな価格を提供します。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation |
| 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: LLM-Powered Test Infrastructure | 10% | - Architecture and deployment considerations - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools |
| Topic 4: Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models |
| Topic 5: 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 |
確かにISQI CT-GenAI試験に準備する過程は苦しいんですけど、ISQI CT-GenAI資格認定を手に入れるなり、IT業界で仕事のより広い将来性を持っています。あなたの努力を無駄にするのは我々MogiExamのすべきことです。MogiExamのレビューから見ると、弊社MogiExamは提供している質高い試験資料は大勢の顧客様の認可を受け取ったと考えられます。我々はあなたにISQI CT-GenAI試験に合格させるために、全力を尽くします。
質問 # 23
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?
正解：A
解説：
The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.
質問 # 24
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：C
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 # 25
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：A
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 # 26
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?
正解：A
質問 # 27
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
正解：A
質問 # 28
......
我々のCT-GenAI試験に何か疑問があったら、我々の係員をオンラインで連絡してください。ほかの人の話しより自分で体験したほうがいいと言われています。我々のサイトで無料なCT-GenAI問題集のサンプルが提供されています。あなたは我々の言うことが依然として信じられないなら、我々のサンプルを無料でダウンロードしてみることができます。
CT-GenAI日本語受験攻略: https://www.mogiexam.com/CT-GenAI-exam.html
P.S.MogiExam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=1Yoblzvh682FP88RkdRB4BMTJr7HwqlWy