A demo answers the only question that matters before purchase: is this useful for me? The PassCollection free demo shows real ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 questions in the actual format — judge for yourself.
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 Price: | ~150 - 200 USD (varies by region) |
| Available Languages: | Portuguese, English, German, Chinese, Spanish |
| Passing Score: | 30 / 46 points (65%) |
| Exam Format: | Multiple choice, 1–2 points per question, Single / multiple correct answers |
| Exam Duration: | 60 (+25% for non-native language speakers) |
| Related Certifications: | ISTQB Advanced Level Certifications ISTQB Expert Level Certifications ISTQB Certified Tester Foundation Level (CTFL) |
| Certificate Validity Period: | Lifetime |
| Real Exam Qty: | 40 |
| Recommended Training: | ISTQB Accredited Training Providers |
| Exam Registration: | iSQI Official Registration |
| 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 |
|---|---|---|
| Prompt Engineering for Effective Software Testing | 35% | - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts |
| Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Hallucinations, bias, inaccuracy, and consistency risks - Validation, verification, and mitigation strategies |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - AI agents and integration with test tools - Architecture and deployment considerations |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Measuring value and continuous improvement - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness |
| Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Exam: Demo First, Decide Second
Must hold ISTQB Certified Tester Foundation Level (CTFL) certification Since requirements change over time, confirm the latest on the official ISQI exam page before scheduling.
The ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam contains 40 to complete within 60 (+25% for non-native language speakers). Timed simulation in the PassCollection Desktop Test Engine teaches that rhythm before exam day does.
The official ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 outline defines 5 domains. The leading three are LLM-Powered Test Infrastructure (10%), Managing Risks of Generative AI in Software Testing (25%), and Introduction to Generative AI for Software Testing (15%). The full list is in the topics section above, and PassCollection's 42 practice questions cover each one.
Online remote proctored (iSQI FLEX) or in-person at test centres Register for the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam through these official channels:
After booking, PassCollection delivers your practice questions within 1 minute — preparation starts the same day.
The ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam requires 30 / 46 points (65%) to pass, and registration costs ~150 - 200 USD (varies by region). A failed attempt means paying the same fee again — a readiness check with the PassCollection engines before booking is the cheaper option.
The CT-GenAI exam is a ISQI certification exam validating the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 syllabus shown above. It belongs to these credential paths: ISTQB Certified Tester Foundation Level (CTFL), ISTQB Advanced Level Certifications, ISTQB Expert Level Certifications. Candidates in 2026 start with PassCollection's 42 practice questions, available as a printable PDF, a Windows Desktop Test Engine, and an Online Test Engine — three versions of the same materials.
ISQI recommends these training resources for the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam:
Follow the training with steady self-testing — the 42 practice questions from PassCollection help you apply theory and fill any gaps the training leaves.
PassCollection issues a full refund if you take the ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam within 60 days of purchase and do not pass. The policy does not apply if the exam is taken within 3 days of purchase, if the exam was never actually taken, or to free materials or expired orders, and the candidate name must match the payer name. Submit a scan of your enrollment slip and the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Alternatively, exchange for two free exam products of equal value and keep your original update service. Orders arrive by email within 1 minute — contact support if nothing arrives within 2 hours.
Three versions of the same study materials: a printable, expert-prepared PDF with free demo download and instant access; a Desktop Test Engine for Windows with customizable simulation and timed modes that works offline; and an Online Test Engine for any browser on Windows, Mac, Android, and iOS with test history and performance review. All 42 practice questions are identical across versions. Every purchase includes 365 days of free updates, a 50% renewal discount afterward, and unlimited computer installations.
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?
- A. Reasoning error in risk calculation logic
- B. Hallucination
- C. Dataset bias toward UI features
- D. No defect; this is acceptable
Correct Answer: A 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
Which statement BEST contrasts interaction style and scope?
- A. Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing
- B. Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
- C. Both are identical aside from UI theme.
- D. Chatbots require API integration; LLM apps do not.
Correct Answer: B 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
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. i -> iv -> iii -> ii
- C. iv -> i -> iii -> ii
- D. iv -> iii -> i -> ii
Correct Answer: C 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
Which AI approach requires feature engineering and structured data preparation?
- A. Symbolic AI
- B. Generative AI
- C. Classical Machine Learning
- D. Deep Learning
Correct Answer: C 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
Which statement about fine-tuning for test tasks is INCORRECT?
- A. It adapts a pre-trained model to a domain using task-specific data
- B. It replaces the model's general knowledge entirely and prevents overfitting
- C. It enhances relevance to organizational terminology and formats
- D. It can be applied to smaller SLMs to improve task performance with lower compute
Correct Answer: B 🗳️
Explanation: Only visible for PassCollection members. You can sign-up / login (it's free).
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