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

Exam Code: CT-GenAI

Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

Updated: Aug 26, 2026

Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
B) Precision for anomaly identification and recall for coverage of valid/invalid partitions
C) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
D) Diversity for anomaly identification and precision for partitions


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


3. What BEST protects sensitive test data at rest and in transit?

A) Disable TLS and rely on VPN only
B) Use public file shares with read-only links
C) Enforce role-based access controls
D) Rely on obfuscation instead of encryption


4. 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, ii, iii
D) i, iii, v


5. Which statement BEST contrasts interaction style and scope?

A) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
B) Chatbots require API integration; LLM apps do not.
C) Both are identical aside from UI theme.
D) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing


Solutions:

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

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