ISQI CT-GenAI Questions & Answers - in .pdf
- Total Q&A: 42
- Update: Aug 18, 2026
- Price: $59.99
- Vendor: ISQI
- Exam Code: CT-GenAI
- Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
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ISQI CT-GenAI Q&A - Testing Engine
- Total Q&A: 42
- Update: Aug 18, 2026
- Price: $59.99
- Vendor: ISQI
- Exam Code: CT-GenAI
- Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0
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ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Topic 1: Tools for Testing Generative AI | 20% | - Testing Tools Overview
- 1. Categories of GenAI testing tools
- 2. Selecting appropriate tools for specific testing needs
- Using Tools for Common Testing Activities
- 1. Security testing tools
- 2. Simulation and monitoring tools
- 3. Prompt testing tools
- 4. Model evaluation tools
|
| Topic 2: Fundamentals of Generative AI | 20% | - Generative AI Concepts
- 1. Model types (Base, Instruction-tuned, RAG)
- 2. Alignment and guardrails
- 3. Hallucinations
- 4. AI model behavior
- 5. Emergent capabilities and limitations
- 6. Training data and context windows
- AI Terminology
- 1. Machine Learning (ML)
- 2. Large Language Models (LLMs)
- 3. Generative AI (GenAI)
- 4. Artificial Intelligence (AI)
- 5. Deep Learning
- 6. Transformer architecture
- 7. Tokens and prompts
- AI Development Lifecycle
- 1. Data collection, preparation, and curation
- 2. Deployment and monitoring
- 3. Evaluation
- 4. Model training and fine-tuning
|
| Topic 3: Testing Activities for Generative AI | 30% | - Requirements-Based Testing
- 1. Non-functional requirements for AI-based systems
- 2. AI-related quality requirements
- 3. Functional requirements for AI-based systems
- Model and Output Evaluation
- 1. Metamorphic testing
- 2. Automated evaluation methods
- 3. Human evaluation methods
- 4. Output correctness and quality assessment
- 5. Checkpoint testing
- Prompt-Based Testing
- 1. Test data creation with GenAI
- 2. Test case design using prompts
- 3. Prompt engineering basics
- Traceability and Documentation
- 1. Documentation requirements for AI testing
- 2. Test coverage of AI model components
|
| Topic 4: Risks and Testing Challenges for Generative AI | 30% | - Testing Challenges for Generative AI
- 1. Complexity of the AI component
- 2. Ethical testing concerns
- 3. Coverage challenges
- 4. Subjectivity of quality assessment
- 5. Test oracle problem
- 6. Regulatory and compliance considerations
- 7. Non-deterministic output behavior
- Quality Risks Specific to Generative AI
- 1. Inappropriate output for the context
- 2. Dependency on external components
- 3. Input sensitivity (prompt brittleness)
- 4. Inconsistent responses across runs
- 5. Offensive, harmful, or biased content
- 6. Incorrect or fabricated outputs (hallucinations)
|
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?
A) Few-shot prompting
B) Zero-shot prompting
C) Meta prompting
D) Prompt chaining
2. An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?
A) Request manipulation
B) Malicious code generation
C) Data poisoning
D) Data exfiltration
3. When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?
A) Achieving faster responses by reducing model checkpoints and updates
B) Focusing primarily on user experience improvements and response formatting
C) Maintaining data privacy and minimizing security risks from external services
D) Maximizing scalability by deploying larger cloud-based LLM clusters
4. 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) Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
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) Specify that the role is a test analyst with expertise in predictive analytics and risk management.
5. 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) iii -> iv -> i -> ii
B) iv -> iii -> i -> ii
C) i -> iv -> iii -> ii
D) iv -> i -> iii -> ii
Solutions:
Question # 1
Answer: A | Question # 2
Answer: D | Question # 3
Answer: C | Question # 4
Answer: C | Question # 5
Answer: D |
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