Hot ISQI CT-GenAI Spot Questions, CT-GenAI Real Braindumps

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ISQI CT-GenAI Exam Syllabus Topics:

Section Weight Objectives
Tools for Testing Generative AI 20% - 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. Simulation and monitoring tools
  • 4. Model evaluation tools
Fundamentals of Generative AI 20% - Generative AI Concepts
  • 1. Training data and context windows
  • 2. Model types (Base, Instruction-tuned, RAG)
  • 3. Alignment and guardrails
  • 4. Hallucinations
  • 5. Emergent capabilities and limitations
  • 6. AI model behavior
- AI Terminology
  • 1. Transformer architecture
  • 2. Deep Learning
  • 3. Large Language Models (LLMs)
  • 4. Generative AI (GenAI)
  • 5. Tokens and prompts
  • 6. Artificial Intelligence (AI)
  • 7. Machine Learning (ML)
- AI Development Lifecycle
  • 1. Model training and fine-tuning
  • 2. Evaluation
  • 3. Deployment and monitoring
  • 4. Data collection, preparation, and curation
Risks and Testing Challenges for Generative AI 30% - Quality Risks Specific to Generative AI
  • 1. Dependency on external components
  • 2. Inappropriate output for the context
  • 3. Offensive, harmful, or biased content
  • 4. Inconsistent responses across runs
  • 5. Incorrect or fabricated outputs (hallucinations)
  • 6. Input sensitivity (prompt brittleness)
- Testing Challenges for Generative AI
  • 1. Regulatory and compliance considerations
  • 2. Coverage challenges
  • 3. Test oracle problem
  • 4. Subjectivity of quality assessment
  • 5. Non-deterministic output behavior
  • 6. Complexity of the AI component
  • 7. Ethical testing concerns
Testing Activities for Generative AI 30% - Model and Output Evaluation
  • 1. Human evaluation methods
  • 2. Automated evaluation methods
  • 3. Checkpoint testing
  • 4. Metamorphic testing
  • 5. Output correctness and quality assessment
- Traceability and Documentation
  • 1. Test coverage of AI model components
  • 2. Documentation requirements for AI testing
- Prompt-Based Testing
  • 1. Prompt engineering basics
  • 2. Test data creation with GenAI
  • 3. Test case design using prompts
- Requirements-Based Testing
  • 1. Non-functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Functional requirements for AI-based systems

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q17-Q22):

NEW QUESTION # 17
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

  • A. Specify that the role is a test architect specializing in CI/CD pipelines.
  • B. Include references to version control systems like Git in the constraints.
  • C. Add a step to review the change log for syntax errors before analysis.
  • D. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Answer: D

Explanation:
The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.


NEW QUESTION # 18
Which setting can reduce variability by narrowing the sampling distribution during inference?

  • A. Increasing temperature
  • B. Increasing learning rate
  • C. Using a larger context window
  • D. Lowering temperature

Answer: D

Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.


NEW QUESTION # 19
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

  • A. Mastering prompt engineering
  • B. Configuring network routers
  • C. Designing custom CPU instructions
  • D. Writing low-level device drivers

Answer: A

Explanation:
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.


NEW QUESTION # 20
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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

Answer: A

Explanation:
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.


NEW QUESTION # 21
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

  • A. Some GenAI tools may store/process data without explicit consent
  • B. Strict GDPR compliance eliminates all privacy risk
  • C. Using GenAI without regulatory compliance can lead to legal exposure
  • D. GenAI outputs can accidentally reveal sensitive information present in inputs

Answer: B

Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.


NEW QUESTION # 22
......

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