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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Risks and Testing Challenges for Generative AI | 30% | - Quality Risks Specific to Generative AI
|
| Topic 2: Testing Activities for Generative AI | 30% | - Model and Output Evaluation
|
| Topic 3: Tools for Testing Generative AI | 20% | - Using Tools for Common Testing Activities
|
| Topic 4: Fundamentals of Generative AI | 20% | - Generative AI Concepts
|
当社CertJukenのCT-GenAI学習準備は、自己学習、自己評価、統計レポート、タイミング、およびテスト刺激機能を強化し、各機能はクライアントが包括的に学習するのに役立つ独自の役割を果たします。 CT-GenAIガイド資料の自己学習および自己評価機能は、クライアントがCT-GenAI学習資料の学習結果を確認するのに役立ちます。 CT-GenAIトレーニングクイズのタイミング機能は、学習者が速度を調整して質問に答え、ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0アラートを維持するのに役立ちます。学習教材はタイマーを設定します。
質問 # 15
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
正解：C
解説：
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.
質問 # 16
What defines a prompt pattern in the context of structured GenAI capability building?
正解：B
解説：
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.
質問 # 17
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：C
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 # 18
Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?
正解：A
解説：
Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.
質問 # 19
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：B
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 # 20
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CertJukenのCT-GenAIスタディガイドには、さまざまなニーズを満たすことができる3つの形式があります。PDFバージョン、ソフトウェアバージョン、オンラインバージョンです。 PDFバージョンを選択した場合は、CT-GenAI学習資料をダウンロードして、どこでも学習できるように印刷できます。新しいバージョンがリリースされた場合は、電子メールボックスへの新しいリンクが送信され、再度ダウンロードできます。ソフトウェアバージョンのCT-GenAI試験教材を使用すると、実際のISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0試験と同じような環境で練習できます。また、CT-GenAI実践ガイドのAPPバージョンは、あらゆる種類の電子機器で利用できます。
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