ISQI CT-GenAI Q&A - in .pdf

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 27, 2026
  • Q & A: 42 Questions and Answers
  • PDF Price: $59.99
  • Printable ISQI CT-GenAI PDF Format. It is an electronic file format regardless of the operating system platform.
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ISQI CT-GenAI Q&A - Testing Engine

  • Exam Code: CT-GenAI
  • Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
  • Updated: Aug 27, 2026
  • Q & A: 42 Questions and Answers
  • Install on multiple computers for self-paced, at-your-convenience training.
  • PC Test Engine Price: $59.99
  • Testing Engine

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About ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 - CT-GenAI Exam Actual Tests

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CT-GenAI exam dumps

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

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


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


3. What does an embedding represent in an LLM?

A) Logical rules for reasoning
B) Numerical vectors capturing semantic relationships
C) A set of test cases for validation
D) Tokens grouped into context windows


4. 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?

A) Generate prioritized conditions in one shot -> verify coverage
B) Prioritize requirements -> generate conditions -> review defects
C) Generate conditions -> prioritize by risk -> map to requirements to find gaps
D) Detect requirement defects -> generate conditions -> prioritize


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

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


Solutions:

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

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