ISQI CT-GenAI Questions & Answers - in .pdf
- Total Q&A: 42
- Update: Sep 12, 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: Sep 12, 2026
- Price: $59.99
- Vendor: ISQI
- Exam Code: CT-GenAI
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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 Price: | ~150 - 200 USD (varies by region) |
| Related Certifications: | ISTQB Advanced Level Certifications ISTQB Expert Level Certifications ISTQB Certified Tester Foundation Level (CTFL) |
| Exam Format: | Single / multiple correct answers, Multiple choice, 1–2 points per question |
| Passing Score: | 30 / 46 points (65%) |
| Real Exam Qty: | 40 |
| Exam Duration: | 60 (+25% for non-native language speakers) |
| Certificate Validity Period: | Lifetime |
| Available Languages: | Chinese, Spanish, English, German, Portuguese |
| 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 |
|---|---|---|
| Managing Risks of Generative AI in Software Testing | 25% | - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies - Hallucinations, bias, inaccuracy, and consistency risks |
| Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Measuring value and continuous improvement - 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 |
| LLM-Powered Test Infrastructure | 10% | - AI agents and integration with test tools - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations |
| Prompt Engineering for Effective Software Testing | 35% | - Principles and structure of effective prompts - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation |
ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:
Question #1
What is a primary compliance concern related to Shadow AI in organizational test environments?
A. Automated compliance validation during AI tool deployment
B. Violation of established data handling and regulatory compliance standards
C. Failure to update system documentation within the test process
D. Difficulty in aligning project milestones with business outcomes
Question #2
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.
Question #3
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?
A. Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints:
Include fixtures, clear assertions, and naming consistent with pytest. | Output Format: Return complete Python test files.
B. Role: Act as a software engineer. | Context: You are testing registration logic. | Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST /api/register with test users. | Constraints: Add checks for status codes. | Output Format: Deliver functional scripts.
C. Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.
D. Role: Act as an automation tester. | Context: You are validating an API endpoint. | Instruction: Generate Python test scripts that send POST requests and validate responses. | Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | Output Format: Provide organized scripts.
Question #4
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. Dataset bias toward UI features
B. No defect; this is acceptable
C. Reasoning error in risk calculation logic
D. Hallucination
Question #5
Which setting can reduce variability by narrowing the sampling distribution during inference?
A. Increasing learning rate
B. Using a larger context window
C. Increasing temperature
D. Lowering temperature
Solutions:
| Question #1 Correct Answer: B | Question #2 Correct Answer: A | Question #3 Correct Answer: A | Question #4 Correct Answer: C | Question #5 Correct Answer: D |
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