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ISQI CT-GenAI Exam Syllabus Topics:

Section	Objectives
Topic 1: Foundations of Generative AI and LLMs	- Introduction to Generative AI in Software Testing 1. Difference between chatbots and LLM-based test tools 2. LLM basics, tokenization, context window, multimodal models
Topic 2: Application of GenAI in Software Testing	- Practical use in testing workflows 1. Test case generation using LLMs 2. Test data generation and augmentation 3. Defect report analysis and summarization 4. Regression suite optimization
Topic 3: Risk, Quality, and Limitations of GenAI	- Risks in GenAI usage 1. Environmental and energy considerations 2. Hallucinations and reasoning errors 3. Bias and fairness issues 4. Data privacy and security concerns
Topic 4: Organizational Adoption and Governance	- Enterprise GenAI adoption 1. Policy, ethics, and compliance considerations 2. LLMOps and governance models 3. Integration into CI/CD pipelines
Topic 5: Prompt Engineering for Testing	- Prompt design techniques 1. Structuring prompts for test case generation 2. Zero-shot, one-shot, few-shot prompting 3. Prompt chaining and meta prompting

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최신 AI Testing CT-GenAI 무료샘플문제 (Q17-Q22):

질문 # 17
What is a key data-related aspect when defining a GenAI strategy for testing?

A. Use only auto-generated synthetic data to avoid dependency on enterprise repositories
B. Aggregate data from all available organizational repositories without filtration
C. Prioritize accurate and relevant input data secured through defined quality procedures
D. Neglect legacy data sources as they provide limited immediate relevance to testing tasks

정답：C

설명：
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.

질문 # 18
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A. Ability to trigger automated actions beyond conversation
B. Ability to respond to prompts without explicit user instructions
C. Use of a conversational tone and improved response personalization
D. Reliance on predefined templates to generate short, factual answers

정답：A

설명：
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.

질문 # 19
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?

A. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
B. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
C. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
D. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.

정답：B

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 # 20
Which statement BEST describes vision-language models (VLMs)?

A. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
B. VLMs are a subset of multimodal LLMs integrating visual and textual information.
C. VLMs are a superset of multimodal LLMs.
D. VLMs process audio and video but not images.

정답：B

설명：
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.

질문 # 21
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A. Reasoning error in risk calculation logic
B. Dataset bias toward UI features
C. Hallucination
D. No defect; this is acceptable

정답：A

설명：
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.

질문 # 22
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지금 사회에 능력자들은 아주 많습니다.it인재들도 더욱더 많아지고 있습니다.많은 it인사들은 모두 관연 it인증시험에 참가하여 자격증취득을 합니다.자기만의 자리를 확실히 지키고 더 높은 자리에 오르자면 필요한 스펙이니까요.CT-GenAI시험은ISQI인증의 중요한 시험이고 또 많은 it인사들은ISQI자격증을 취득하려고 노력하고 있습니다.

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