獲得CT-GenAI認證已經成為大多數IT員工獲得更好工作的一種選擇，然而，許多考生一直在努力嘗試卻失敗了。如果你選擇使用我們的ISQI CT-GenAI題庫產品，幫您最大程度保證取得成功。充分利用CT-GenAI題庫你將得到不一樣的效果，這是一個針對性強，覆蓋面廣，更新快，最完整的學習資料，保證您一次通過CT-GenAI考試。如果您想要真實的考試模擬，就選擇我們軟件版本的ISQI CT-GenAI題庫，安裝在電腦上進行模擬，簡單易操作。
| Section | Objectives |
|---|---|
| Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
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| Application of GenAI in Software Testing | - Practical use in testing workflows
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| Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Prompt Engineering for Testing | - Prompt design techniques
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| Organizational Adoption and Governance | - Enterprise GenAI adoption
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ISQI CT-GenAI 認證作為全球IT領域專家 ISQI 熱門認證之一，是許多大中IT企業選擇人才標準的必備條件。ISQI CT-GenAI 考題由全球領先的IT認證考試中心授權，幫助考生一次性順利取得通過 CT-GenAI 考試；否則將全額退費，這一舉動保證考生權利不受任何的損失。考生考試前需要在全球的Prometric考試中心進行報名並預約考試時間。
問題 #28
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?
答案：D
解題說明：
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.
問題 #29
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：D
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 #30
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement
答案：C
解題說明：
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.
問題 #31
What is a hallucination in LLM outputs?
答案：C
解題說明：
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.
問題 #32
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
答案：C
問題 #33
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KaoGuTi為通過CT-GenAI考試提供最完整有效的方案，幫祝廣大考生在考試中獲得更多的優勢。確保你只獲得最新的和最有效的ISQI CT-GenAI考古題，我們也希望客戶能隨時隨地的訪問，于是有了多個版本的題庫資料。PDF版的題庫方便你閱讀，為你真實地再現CT-GenAI考試題目，軟件版本的題庫作為一個測試引擎，可以幫你模擬真實的CT-GenAI考試環境，為考生做好充足的考前準備。通過ISQI CT-GenAI考試不再是夢想，我們的考古題就可以確保你成功。
CT-GenAI考試內容: https://www.kaoguti.com/CT-GenAI_exam-pdf.html