P.S. JPNTestがGoogle Driveで共有している無料かつ新しいCT-GenAIダンプ：https://drive.google.com/open?id=1yVXrQujlJhXUf3GxloJKNDv0OW0dizxE
当社は、すべての受験者が試験に簡単に合格できるようにCT-GenAI最新の練習教材を開発することに専念しており、10年以上の開発の後に大きな成果を上げています。認定資格は非常に価値が高いため、適切なCT-GenAI試験ガイドは、バターを通過するホットナイフのようなCT-GenAI試験に合格するための強力な推進力となります。そして、CT-GenAI試験ガイドの質の高いCT-GenAI学習ガイドは、98％以上の高い合格率によって証明されているため、CT-GenAI試験問題はまさにあなたにとって正しいものです。
| Section | Weight | Objectives |
|---|---|---|
| Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Capabilities and limitations relevant to testing - Core concepts: Generative AI, LLMs, foundation models |
| Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
| 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 |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap |
クライアントは、支払いが完了するとすぐに、当社の製品をダウンロードし、CT-GenAI学習教材を使用できます。私たちのシステムは、支払いが成功してから5〜10分後にCT-GenAI学習準備をメール形式でクライアントに送信します。メールはリンクを提供します。クライアントのみがリンクをクリックすると、すぐにソフトウェアにログインしてCT-GenAIガイド資料を学習できます。クライアントがCT-GenAIトレーニングクイズを購入する限り、すぐにJPNTest製品を使用して時間を節約できます。
質問 # 25
What is a hallucination in LLM outputs?
正解：D
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 # 26
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：A
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 # 27
Which of the following is NOT a valid form of LLM-driven test data generation?
正解：D
解説：
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.
質問 # 28
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?
正解：B
解説：
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.
質問 # 29
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 # 30
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CT-GenAI実践資料は、これらのCT-GenAI実践資料を説明責任を持って作成した当社のものです。 また、CT-GenAIトレーニング資料は効率的な製品です。 さらに、CT-GenAI試験準備は適切で立派な練習資料です。 進捗状況を確認し、CT-GenAIトレーニング資料の証明書を取得することは、当然のことながら、最新かつ最も正確な知識を備えた最も専門的な専門家によるものです。 CT-GenAI試験準備は市場の大部分を占めています。
CT-GenAI認定試験トレーリング: https://www.jpntest.com/shiken/CT-GenAI-mondaishu
無料でクラウドストレージから最新のJPNTest CT-GenAI PDFダンプをダウンロードする：https://drive.google.com/open?id=1yVXrQujlJhXUf3GxloJKNDv0OW0dizxE