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Success in the test of the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) certification proves your technical knowledge and skills. The CT-GenAI exam credential paves the way toward landing high-paying jobs or promotions in your organization. Many people who attempt the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) exam questions don't find updated practice questions. Due to this they don't prepare as per the current CT-GenAI examination content and fail the final test.

ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q33-Q38):

NEW QUESTION # 33
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Answer: A

Explanation:
Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
"project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.


NEW QUESTION # 34
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: D

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 # 35
Which statement BEST describes vision-language models (VLMs)?

Answer: B

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 # 36
Which setting can reduce variability by narrowing the sampling distribution during inference?

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 # 37
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

Answer: B

Explanation:
ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
"requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.


NEW QUESTION # 38
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