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ISQI CT-GenAI認定資格試験が難しいので、弊社のCT-GenAI問題集はあなたに適当する認定資格試験問題集を見つけるし、本当の試験問題の難しさを克服することができます。弊社はISQI CT-GenAI認定試験の最新要求に従って関心を持って、全面的かつ高品質な模擬試験問題集を提供します。また、購入する前に、無料でCT-GenAIのPDF版デモをダウンロードでき、信頼性を確認することができます。
| Section | Objectives |
|---|---|
| Topic 1: Application of GenAI in Software Testing | - Practical use in testing workflows
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| Topic 2: Organizational Adoption and Governance | - Enterprise GenAI adoption
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| Topic 3: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Topic 4: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
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| Topic 5: Prompt Engineering for Testing | - Prompt design techniques
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CT-GenAI試験に簡単に合格し、最短時間で認定資格を取得したい場合、最良の方法は、最高品質のCT-GenAI試験準備資料を購入することです。それが私たちのすることです。 CT-GenAIトレーニング資料は、この分野で高い合格率を誇ることで有名です。当社の製品を選択した場合、CT-GenAI試験を100％クリアできると確信しています。確実に試験に合格する方法についてまだ頭痛の種である場合、CT-GenAI模擬試験の質問が最良の選択です。 heしないで、私たちを選んでください！
質問 # 29
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
正解：B
解説：
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.
質問 # 30
Which AI approach requires feature engineering and structured data preparation?
正解：A
解説：
Classical Machine Learning(which includes algorithms like Random Forests, Support Vector Machines, and Linear Regression) is characterized by its reliance onFeature Engineering. This is the process where human experts manually select, extract, and transform raw data into a set of "features" or variables that the algorithm can process. For instance, in a classical ML model predicting software defects, a tester might have to manually define features like "lines of code changed" or "number of previous bugs." In contrast,Deep Learningand its subset,Generative AI(Options B and D), utilize "Representation Learning." This means the multi-layered neural networks automatically identify and extract the relevant features from raw, often unstructured data (like text or images) without explicit human instruction.Symbolic AI(Option A) is based on hard-coded logical rules rather than data-driven learning. Understanding this distinction is fundamental for testers, as it determines the level of data preparation required: Classical ML requires high human effort in data structuring, while GenAI requires high effort in prompt engineering and grounding.
質問 # 31
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 # 32
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
正解：C
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 # 33
Which statement BEST describes vision-language models (VLMs)?
正解：B
解説：
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.
質問 # 34
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