Updated: Aug 25, 2026
No. of Questions: 42 Questions & Answers with Testing Engine
Download Limit: Unlimited
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| 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) |
| Related Certifications: | ISTQB Certified Tester Foundation Level (CTFL) ISTQB Advanced Level Certifications ISTQB Expert Level Certifications |
| Available Languages: | English, German, Spanish, Portuguese, Chinese |
| Exam Duration: | 60 (+25% for non-native language speakers) |
| Certificate Validity Period: | Lifetime |
| Exam Format: | Multiple choice, Single / multiple correct answers, 1–2 points per question |
| Passing Score: | 30 / 46 points (65%) |
| Real Exam Qty: | 40 |
| 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/ |
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
| Topic 2: Introduction to Generative AI for Software Testing | 15% | - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models - Capabilities and limitations relevant to testing |
| Topic 3: Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies |
| Topic 4: Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Prompt patterns for test design, data generation, automation - Iterative refinement and evaluation of prompts |
| Topic 5: LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
1. Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?
A) Select LLMs aligned to measurable test outcomes, compatible with current infrastructure
B) Select models with maximum vendor visibility and strong online presence to ensure reliability
C) Select broad-coverage models offering diverse functionalities for various test scenarios
D) Select open-source models prioritizing creativity over compliance or performance consistency
2. 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) No defect; this is acceptable
B) Reasoning error in risk calculation logic
C) Hallucination
D) Dataset bias toward UI features
3. 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 exfiltration
D) Data poisoning
4. 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
5. 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) Add a step to review the change log for syntax errors before analysis.
B) Specify that the role is a test architect specializing in CI/CD pipelines.
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
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: B | Question # 3 Answer: C | Question # 4 Answer: B | Question # 5 Answer: D |
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