ISQI CT-GenAI Questions & Answers - in .pdf
- Total Q&A: 42
- Update: Aug 17, 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 |
|---|
| Testing Activities for Generative AI | 30% | - Model and Output Evaluation
- 1. Human evaluation methods
- 2. Automated evaluation methods
- 3. Checkpoint testing
- 4. Metamorphic testing
- 5. Output correctness and quality assessment
- 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
- Requirements-Based Testing
- 1. AI-related quality requirements
- 2. Non-functional requirements for AI-based systems
- 3. Functional requirements for AI-based systems
|
| 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. Simulation and monitoring tools
- 2. Prompt testing tools
- 3. Model evaluation tools
- 4. Security testing tools
|
| Risks and Testing Challenges for Generative AI | 30% | - Quality Risks Specific to Generative AI
- 1. Inconsistent responses across runs
- 2. Offensive, harmful, or biased content
- 3. Dependency on external components
- 4. Inappropriate output for the context
- 5. Incorrect or fabricated outputs (hallucinations)
- 6. Input sensitivity (prompt brittleness)
- Testing Challenges for Generative AI
- 1. Regulatory and compliance considerations
- 2. Ethical testing concerns
- 3. Test oracle problem
- 4. Complexity of the AI component
- 5. Coverage challenges
- 6. Non-deterministic output behavior
- 7. Subjectivity of quality assessment
|
| Fundamentals of Generative AI | 20% | - AI Terminology
- 1. Generative AI (GenAI)
- 2. Deep Learning
- 3. Tokens and prompts
- 4. Machine Learning (ML)
- 5. Artificial Intelligence (AI)
- 6. Transformer architecture
- 7. Large Language Models (LLMs)
- Generative AI Concepts
- 1. Hallucinations
- 2. Alignment and guardrails
- 3. Emergent capabilities and limitations
- 4. AI model behavior
- 5. Model types (Base, Instruction-tuned, RAG)
- 6. Training data and context windows
- AI Development Lifecycle
- 1. Evaluation
- 2. Data collection, preparation, and curation
- 3. Deployment and monitoring
- 4. Model training and fine-tuning
|
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
1. Which statement BEST describes vision-language models (VLMs)?
A) VLMs are a subset of multimodal LLMs integrating visual and textual information.
B) VLMs process audio and video but not images.
C) VLMs are a superset of multimodal LLMs.
D) VLMs are unrelated to multimodal LLMs and focus only on UI automation.
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) Specify that the role is a test architect specializing in CI/CD pipelines.
B) Add a step to review the change log for syntax errors before analysis.
C) Include references to version control systems like Git in the constraints.
D) Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
3. 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.
4. Which statement BEST contrasts interaction style and scope?
A) Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
B) Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
C) Chatbots require API integration; LLM apps do not.
D) Both are identical aside from UI theme.
5. 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) Prioritize requirements -> generate conditions -> review defects
B) Detect requirement defects -> generate conditions -> prioritize
C) Generate conditions -> prioritize by risk -> map to requirements to find gaps
D) Generate prioritized conditions in one shot -> verify coverage
Solutions:
Question # 1
Answer: A | Question # 2
Answer: D | Question # 3
Answer: C | Question # 4
Answer: A | Question # 5
Answer: C |
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