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Updated: Aug 21, 2026

No. of Questions: 42 Questions & Answers with Testing Engine

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ISQI CT-GenAI Exam Overview:

Certification Vendor:ISQI / ASTQB
Exam Name:ISTQB Certified Tester Testing with Generative AI
Exam Number:CT-GenAI
Exam Price:250 EUR (+ VAT)
Related Certifications:ISTQB CTFL (Foundation Level Tester)
Exam Duration:60 minutes
Available Languages:English, German
Exam Format:Multiple Choice
Passing Score:65%
Real Exam Qty:40
Certificate Validity Period:Not publicly specified (typically 3-5 years depending on scheme)
Sample Questions:ISQI CT-GenAI Sample Questions
Exam Way:Online proctored / In-person at authorized test centers
Pre Condition:ISTQB CTFL (Foundation Level Tester) certification is recommended but not required
Official Syllabus URL:https://www.astqb.org/ct-genai/

ISQI CT-GenAI Exam Syllabus Topics:

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

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

1. You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

A) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
B) Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
C) Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
D) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.


2. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A) The duration of user sessions primarily affects latency but not power efficiency
B) The number of tokens processed directly determines the carbon intensity of each query
C) The type of cloud platform affects processing speed but not total energy draw
D) The location of the data center determines model bias and accuracy levels


3. You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A) iv -> iii -> i -> ii
B) iv -> i -> iii -> ii
C) iii -> iv -> i -> ii
D) i -> iv -> iii -> ii


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

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


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

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


Solutions:

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

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