從Google Drive中免費下載最新的VCESoft CT-GenAI PDF版考試題庫：https://drive.google.com/open?id=1t-bTv5KzFtVOzHBt5czKAYLofrQpiFMt
我們VCESoft提供的培訓工具包含我們的IT專家團隊研究出來的備考心得和相關的考試材料。也有關於ISQI CT-GenAI認證考試的考試練習題和答案。以我們VCESoft在IT行業中的高信譽度可以給你提供100%的保障，為了讓你更安心的選擇購買我們，你可以先嘗試在網上下載我們提供的關於ISQI CT-GenAI認證考試的部分考題及答案。
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing Activities for Generative AI | 30% | - Traceability and Documentation
|
| Topic 2: Risks and Testing Challenges for Generative AI | 30% | - Testing Challenges for Generative AI
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| Topic 3: Tools for Testing Generative AI | 20% | - Testing Tools Overview
|
| Topic 4: Fundamentals of Generative AI | 20% | - AI Terminology
|
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問題 #17
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
答案：D
解題說明：
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.
問題 #18
Which option BEST differentiates the three prompting techniques?
答案：D
解題說明：
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.
問題 #19
Which statement about fine-tuning for test tasks is INCORRECT?
答案：A
解題說明：
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.
問題 #20
What defines a prompt pattern in the context of structured GenAI capability building?
答案：B
解題說明：
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.
問題 #21
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?
答案：C
解題說明：
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.
問題 #22
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CT-GenAI考古題: https://www.vcesoft.com/CT-GenAI-pdf.html
P.S. VCESoft在Google Drive上分享了免費的、最新的CT-GenAI考試題庫：https://drive.google.com/open?id=1t-bTv5KzFtVOzHBt5czKAYLofrQpiFMt