Free PDF 2026 Accurate ISQI CT-GenAI: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Reliable Exam Vce

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ISQI CT-GenAI Exam Syllabus Topics:

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

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Quiz ISQI - CT-GenAI - Newest ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Reliable Exam Vce

Achieving the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) certification can significantly impact your career progression and earning potential. This certification showcases your expertise and knowledge to employers, making you a valuable asset in the ISQI CT-GenAI industry. With the rapidly evolving nature of the ISQI world, staying up-to-date with the latest technologies and trends is crucial. The CT-GenAI Certification Exam enables you to learn these changes and ensures you remain current in your field.

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q24-Q29):

NEW QUESTION # 24
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?

Answer: C


NEW QUESTION # 25
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: A

Explanation:
A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.


NEW QUESTION # 26
What defines a prompt pattern in the context of structured GenAI capability building?

Answer: D

Explanation:
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.


NEW QUESTION # 27
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 # 28
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Answer: D

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 # 29
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