Valid CT-GenAI Exam Voucher, CT-GenAI Dumps Questions

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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI CT-GenAI Dumps Questions - New CT-GenAI Dumps Questions

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q14-Q19):

NEW QUESTION # 14
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

Answer: B

Explanation:
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.


NEW QUESTION # 15
Which statement BEST describes vision-language models (VLMs)?

Answer: D

Explanation:
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.


NEW QUESTION # 16
What BEST protects sensitive test data at rest and in transit?

Answer: A

Explanation:
Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.


NEW QUESTION # 17
Which statement BEST contrasts interaction style and scope?

Answer: D

Explanation:
It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
"LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.


NEW QUESTION # 18
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?

Answer: D

Explanation:
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.


NEW QUESTION # 19
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