시험준비에가장좋은CT-GenAI최고품질인증시험기출문제덤프최신샘플문제

그리고 Itexamdump CT-GenAI 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1yBteYHHI2gXCyc_MUkFQdxne9PublY-T

ISQI인증 CT-GenAI시험을 등록하였는데 시험준비를 어떻게 해애 될지 몰라 고민중이시라면 이 글을 보고Itexamdump를 찾아주세요. Itexamdump의ISQI인증 CT-GenAI덤프샘플을 체험해보시면 시험에 대한 두려움이 사라질것입니다. Itexamdump의ISQI인증 CT-GenAI덤프는ISQI인증 CT-GenAI실제시험문제를 마스터한 기초에서 제작한 최신시험에 대비한 공부자료로서 시험패스율이 100%입니다. 하루 빨리 덤프를 마련하여 시험을 준비하시면 자격증 취득이 빨라집니다.

ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Fundamentals of Generative AI20%- AI Development Lifecycle
  • 1. Evaluation
  • 2. Deployment and monitoring
  • 3. Model training and fine-tuning
  • 4. Data collection, preparation, and curation
- AI Terminology
  • 1. Generative AI (GenAI)
  • 2. Deep Learning
  • 3. Artificial Intelligence (AI)
  • 4. Tokens and prompts
  • 5. Transformer architecture
  • 6. Machine Learning (ML)
  • 7. Large Language Models (LLMs)
- Generative AI Concepts
  • 1. Hallucinations
  • 2. Emergent capabilities and limitations
  • 3. Alignment and guardrails
  • 4. Training data and context windows
  • 5. Model types (Base, Instruction-tuned, RAG)
  • 6. AI model behavior
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Input sensitivity (prompt brittleness)
  • 2. Inappropriate output for the context
  • 3. Dependency on external components
  • 4. Inconsistent responses across runs
  • 5. Offensive, harmful, or biased content
  • 6. Incorrect or fabricated outputs (hallucinations)
- Testing Challenges for Generative AI
  • 1. Non-deterministic output behavior
  • 2. Coverage challenges
  • 3. Subjectivity of quality assessment
  • 4. Complexity of the AI component
  • 5. Regulatory and compliance considerations
  • 6. Test oracle problem
  • 7. Ethical testing concerns
Testing Activities for Generative AI30%- Requirements-Based Testing
  • 1. Functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Non-functional requirements for AI-based systems
- Prompt-Based Testing
  • 1. Test case design using prompts
  • 2. Test data creation with GenAI
  • 3. Prompt engineering basics
- Traceability and Documentation
  • 1. Test coverage of AI model components
  • 2. Documentation requirements for AI testing
- Model and Output Evaluation
  • 1. Output correctness and quality assessment
  • 2. Automated evaluation methods
  • 3. Metamorphic testing
  • 4. Checkpoint testing
  • 5. Human evaluation methods
Tools for Testing Generative AI20%- Testing Tools Overview
  • 1. Categories of GenAI testing tools
  • 2. Selecting appropriate tools for specific testing needs
- Using Tools for Common Testing Activities
  • 1. Model evaluation tools
  • 2. Prompt testing tools
  • 3. Security testing tools
  • 4. Simulation and monitoring tools

>> CT-GenAI최고품질 인증시험 기출문제 <<

CT-GenAI인기자격증 덤프자료 - CT-GenAI공부자료

Itexamdump의 ISQI CT-GenAI덤프를 구매하기전 우선 pdf버전 덤프샘플을 다운받아 덤프문제를 공부해보시면Itexamdump덤프품질에 신뢰가 느껴질것입니다. Itexamdump의 ISQI CT-GenAI덤프가 고객님의 시험패스테 조금이나마 도움이 되신다면 행복으로 느끼겠습니다.

최신 AI Testing CT-GenAI 무료샘플문제 (Q34-Q39):

질문 # 34
What are the three key phases in adopting GenAI in a test organization?

정답：A

설명：
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


질문 # 35
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

정답：B

설명：
This scenario describes a form ofRequest Manipulation, specifically a type of "Prompt Injection" or
"Adversarial Prompting." In this attack vector, the user (or an external attacker) provides malicious or deceptive input-in this case, via an image in a multimodal LLM-to bypass the model's intended constraints or to steer its logic toward an unintended outcome. By crafting an image that tricks the LLM into seeing
"acceptance criteria" that aren't actually there, the attacker manipulates the model's request processing to generate false validation results. This is different fromData Poisoning(Option A), which involves corrupting the training data before the model is even built. It is also distinct fromData Exfiltration(Option B), which aims to steal data from the model. In a testing environment, request manipulation is a significant risk because it can lead to "Silent Failures," where the AI reports that tests have passed or requirements are met based on deceptive input, thereby compromising the integrity of the entire Quality Assurance process.


질문 # 36
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

정답：D

설명：
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.


질문 # 37
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

정답：B

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 # 38
Which statement BEST describes vision-language models (VLMs)?

정답：C

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 # 39
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Itexamdump 는 완전히 여러분이 인증시험 준비와 안전한 시험패스를 위한 완벽한 덤프제공 사이트입니다.우리 Itexamdump의 덤프들은 응시자에 따라 ,시험 ,시험방법에 따라 알 맞춤한 퍼펙트한 자료입니다.여러분은 Itexamdump의 알맞춤 덤프들로 아주 간단하고 편하게 인증시험을 패스할 수 있습니다.많은 CT-GenAI인증관연 응시자들은 우리 Itexamdump가 제공하는CT-GenAI 문제와 답으로 되어있는 덤프로 자격증을 취득하셨습니다.우리 Itexamdump 또한 업계에서 아주 좋은 이미지를 가지고 있습니다.

CT-GenAI인기자격증 덤프자료: https://www.itexamdump.com/CT-GenAI.html

그리고 Itexamdump CT-GenAI 시험 문제집의 전체 버전을 클라우드 저장소에서 다운로드할 수 있습니다: https://drive.google.com/open?id=1yBteYHHI2gXCyc_MUkFQdxne9PublY-T