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ISQI CT-GenAI Exam Overview:
| Certification Vendor: | iSQI / ISTQB |
|---|---|
| Exam Name: | ISTQB Certified Tester Testing with Generative AI v1.0 |
| Exam Number: | CT-GenAI |
| Exam Format: | Single / multiple correct answers, 1–2 points per question, Multiple choice |
| Available Languages: | Portuguese, German, Spanish, English, Chinese |
| Real Exam Qty: | 40 |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) ISTQB Expert Level Certifications ISTQB Advanced Level Certifications |
| Exam Duration: | 60 (+25% for non-native language speakers) |
| Passing Score: | 30 / 46 points (65%) |
| Exam Price: | ~150 - 200 USD (varies by region) |
| Certificate Validity Period: | Lifetime |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Official Registration |
| Sample Questions: | ISQI CT-GenAI Sample Questions |
| Exam Way: | Online remote proctored (iSQI FLEX) or in-person at test centres |
| Pre Condition: | Must hold ISTQB Certified Tester Foundation Level (CTFL) certification |
| Official Syllabus URL: | https://istqb.org/certifications/gen-ai/ |
ISQI CT-GenAI Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Managing Risks of Generative AI in Software Testing | 25% | - Validation, verification, and mitigation strategies - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks |
| Topic 2: Deploying and Integrating GenAI in Test Organisations | 15% | - Roles, skills, and team readiness - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement |
| Topic 3: LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations |
| Topic 4: Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation |
| Topic 5: Introduction to Generative AI for Software Testing | 15% | - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Question #1
What is a key data-related aspect when defining a GenAI strategy for testing?
- A. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
- B. Prioritize accurate and relevant input data secured through defined quality procedures
- C. Use only auto-generated synthetic data to avoid dependency on enterprise repositories
- D. Aggregate data from all available organizational repositories without filtration
Answer: B
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Question #2
What defines a prompt pattern in the context of structured GenAI capability building?
- A. Maintaining static documentation repositories without real-time prompt standardization processes
- B. Using ad hoc prompts without reference to previously proven structures or examples
- C. Treating prompts as access credentials or compliance records rather than functional templates
- D. Applying a reusable and structured template that guides GenAI models toward consistent outputs
Answer: D
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Question #3
What does an embedding represent in an LLM?
- A. Tokens grouped into context windows
- B. Logical rules for reasoning
- C. A set of test cases for validation
- D. Numerical vectors capturing semantic relationships
Answer: D
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Question #4
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?
- A. EU AI Act
- B. ISO/IEC 42001:2023
- C. ISO/IEC 23053:2022
- D. NIST AI RMF 1.0
Answer: B
Explanation: Only visible for SurePassExams members. You can sign-up / login (it's free).
Question #5
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
- A. Ability to trigger automated actions beyond conversation
- B. Use of a conversational tone and improved response personalization
- C. Ability to respond to prompts without explicit user instructions
- D. Reliance on predefined templates to generate short, factual answers
Answer: A
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