ISQI CT-GenAI「ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0」認証試験に合格することが簡単ではなくて、ISQI CT-GenAI証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。
| Section | Objectives |
|---|---|
| Topic 1: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 2: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Topic 3: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
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| Topic 4: Application of GenAI in Software Testing | - Practical use in testing workflows
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| Topic 5: Organizational Adoption and Governance | - Enterprise GenAI adoption
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どのようにすればもっと楽にISQIのCT-GenAI認定試験に合格することができるかについて考えたことがありますか。試験に合格する秘密を見つけましたか。それを行う方法がわからない場合、私は教えてあげましょう。実際には、認定試験に合格できる方法が多くあります。試験に関連する知識を一生懸命習得することがただ一つの方法です。今はそのようにしていますか。しかし、これが一番時間を無駄にして、望ましい効果を得られない方法です。それに、毎日仕事で忙しいあなたは、恐らく試験に準備する充分な時間がないでしょう。では、CertJukenのCT-GenAI問題集を試しましょう。この試験参考書はきっとあなたに思えぬ良い結果を与えられます。
質問 # 18
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：D
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 # 19
Which technique MOST directly reduces hallucinations by grounding the model in project realities?
正解：B
解説：
Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
"project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.
質問 # 20
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
正解：D
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 # 21
What is a hallucination in LLM outputs?
正解：A
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 # 22
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?
正解：A
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 # 23
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CT-GenAIテスト教材は、主に3つの学習モード（Pdf、オンライン、ソフトウェア）をそれぞれ使用します。その中でも、ソフトウェアモデルはコンピューターユーザー向けに設計されており、ユーザーがWindowsインターフェイスを使用して学習のCT-GenAIテスト準備を開くことができます。ユーザーが読むのに便利です。 CT-GenAIテスト教材には、オンライン学習プラットフォームとは異なる最大の利点があります。CT-GenAIクイズトレントは、クライアントにログインして同時に詳細を学習することができ、人々はCT-GenAIあらゆる種類の電子機器のテスト準備。
CT-GenAI専門知識: https://www.certjuken.com/CT-GenAI-exam.html