一番優秀なCT-GenAI認定デベロッパーと更新するCT-GenAI前提条件

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長年の努力と革新とクライアントベースのコンセプトを中心に、当社は業界の旗艦企業に成長しました。当社は、CT-GenAI試験の準備の質の向上に苦労し、CT-GenAIスタディガイドの研究と革新に多大な努力とお金を投資しています。業界での当社のブランド名は、優れたCT-GenAI学習ガイドで有名です。高品質、思いやりのあるサービス、絶え間ない革新、そしてCT-GenAI試験問題での最初の顧客の概念は、当社の4つの柱です。

ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
  • 1. Bias and fairness issues
    • 2. Environmental and energy considerations
      • 3. Data privacy and security concerns
        • 4. Hallucinations and reasoning errors
          Foundations of Generative AI and LLMs- Introduction to Generative AI in Software Testing
          • 1. Difference between chatbots and LLM-based test tools
            • 2. LLM basics, tokenization, context window, multimodal models
              Application of GenAI in Software Testing- Practical use in testing workflows
              • 1. Test data generation and augmentation
                • 2. Test case generation using LLMs
                  • 3. Defect report analysis and summarization
                    • 4. Regression suite optimization
                      Prompt Engineering for Testing- Prompt design techniques
                      • 1. Zero-shot, one-shot, few-shot prompting
                        • 2. Prompt chaining and meta prompting
                          • 3. Structuring prompts for test case generation
                            Organizational Adoption and Governance- Enterprise GenAI adoption
                            • 1. Policy, ethics, and compliance considerations
                              • 2. Integration into CI/CD pipelines
                                • 3. LLMOps and governance models

                                  >> CT-GenAI認定デベロッパー <<

                                  CT-GenAI前提条件 & CT-GenAI日本語版対応参考書

                                  我々社のISQI CT-GenAI問題集を購入するかどうかと疑問があると、弊社CertShikenのCT-GenAI問題集のサンプルをしてみるのもいいことです。試用した後、我々のCT-GenAI問題集はあなたを試験に順調に合格させると信じられます。なぜと言うのは、我々社の専門家は改革に応じて問題の更新と改善を続けていくのは出発点から勝つからです。

                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q15-Q20):

                                  質問 # 15
                                  What BEST protects sensitive test data at rest and in transit?

                                  正解：A

                                  解説：
                                  Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.


                                  質問 # 16
                                  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 # 17
                                  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 # 18
                                  Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

                                  正解：A

                                  解説：
                                  As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.


                                  質問 # 19
                                  A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

                                  正解：B

                                  解説：
                                  In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
                                  "Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
                                  "Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.


                                  質問 # 20
                                  ......

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