ITDumpsKR는 IT인증시험 자격증 공부자료를 제공해드리는 전문적인 사이트입니다. ITDumpsKR제품은 100%통과율을 자랑하고 있습니다. ISQI인증 CT-GenAI시험이 어려워 자격증 취득을 망설이는 분들이 많습니다. ITDumpsKR가 있으면 이런 걱정은 하지 않으셔도 됩니다. ITDumpsKR의ISQI인증 CT-GenAI덤프로 시험을 한방에 통과하여 승진이나 연봉인상에 도움되는 자격증을 취득합시다.
| Section | Objectives |
|---|---|
| Topic 1: Application of GenAI in Software Testing | - Practical use in testing workflows
|
| Topic 2: Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Topic 3: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
| Topic 4: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 5: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
>> ISQI CT-GenAI시험대비 덤프데모 다운 <<
ISQI CT-GenAI인증시험은 전업적지식이 강한 인증입니다. IT업계에서 일자리를 찾고 계시다면 많은 회사에서는ISQI CT-GenAI있는지 없는지에 알고 싶어합니다. 만약ISQI CT-GenAI자격증이 있으시다면 여러분은 당연히 경쟁력향상입니다.
질문 # 29
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?
정답：C
설명：
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.
질문 # 30
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?
정답：D
설명：
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.
질문 # 31
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?
정답：D
설명：
As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.
질문 # 32
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?
정답：A
설명：
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.
질문 # 33
Which option BEST differentiates the three prompting techniques?
정답：C
설명：
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.
질문 # 34
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ISQI인증 CT-GenAI시험을 한방에 편하게 통과하여 자격증을 취득하려면 시험전 공부가이드가 필수입니다. ITDumpsKR에서 연구제작한 ISQI인증 CT-GenAI덤프는ISQI인증 CT-GenAI시험을 패스하는데 가장 좋은 시험준비 공부자료입니다. ITDumpsKR덤프공부자료는 엘리트한 IT전문자들이 자신의 노하우와 경험으로 최선을 다해 연구제작한 결과물입니다.IT인증자격증을 취득하려는 분들의 곁은ITDumpsKR가 지켜드립니다.
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