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ISQI CT-GenAI valid exam - in .pdf Free Demo

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Last Updated: Aug 05, 2026
  • Q & A: 42 Questions and Answers
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  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
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  • Q & A: 42 Questions and Answers
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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

A) Data poisoning
B) Malicious code generation
C) Data exfiltration
D) Request manipulation


2. Which concept refers to breaking text into smaller units for processing by LLMs?

A) Tokenization
B) Transformer
C) Context Window
D) Embeddings


3. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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


4. Which setting can reduce variability by narrowing the sampling distribution during inference?

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


5. In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

A) i, iii, v
B) i, ii, iii
C) ii, iii, iv
D) i, ii, iii (Duplicate entry in original source)


Solutions:

Question # 1
Answer: C
Question # 2
Answer: A
Question # 3
Answer: D
Question # 4
Answer: D
Question # 5
Answer: B

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