[100% Off] Istqb Testing - Generativeai (Ct-Genai) 240 - Mock Test 2025

ISTQB CT-GenAI Exam Practice – 6 Full Mock Exams Aligned with Latest Syllabus | Pass on First Attempt

Added on October 28, 2025 IT & Software 10 min read

What you’ll learn

Prepare thoroughly for the ISTQB CT-GenAI certification exam with 6 full-length practice tests.
Master prompt engineering and structured prompting for software testing with Generative AI.
Identify and mitigate risks like hallucinations
bias
privacy
and non-determinism in LLM outputs.
Understand LLM-powered test infrastructure
RAG
and fine-tuning for testing tasks.
Learn how to adopt and integrate Generative AI in test organizations responsibly.
Track performance
analyze results
and strengthen weak areas with detailed explanations.
Apply scenario-based reasoning to tackle complex
real-world software testing challenges.
Gain hands-on experience in designing AI-assisted test cases for both microservices and enterprise systems.
Develop skills to assess AI model outputs
verify correctness
and maintain test quality
Build confidence in time management and exam discipline under realistic
timed conditions.
Understand GenAI adoption strategies
organizational readiness
and AI ethics in testing projects.
Prepare for advanced roles in QA
automation
and AI-powered testing environments with a globally recognized certification.

Requirements

ISTQB Foundation Level Certification (mandatory prerequisite).
Basic knowledge of software testing concepts and processes.
Familiarity with AI/ML terminology is helpful but not mandatory.
A computer with internet access for practice exams and online learning.
Motivation to specialize in Generative AI testing and advance career paths.

Description

Are you preparing for the ISTQB Certified Tester – Testing with Generative AI (CT-GenAI) certification and want to assess your readiness with realistic, high-quality exam-style practice questions?

This comprehensive practice exam course has been designed to mirror the real CT-GenAI certification exam as closely as possible.

With 6 full-length practice tests containing 240 questions in total, you will gain the confidence and knowledge required to pass the ISTQB CT-GenAI certification on your very first attempt. Each question is carefully written to match the difficulty, structure, and exam-style wording you will face on test day.

Every question comes with detailed explanations for both correct and incorrect answers, ensuring that you not only know the right answer but also understand why the other options are wrong. This unique approach deepens your understanding and prepares you for any variation of the question that may appear in the real exam.

Our ISTQB CT-GenAI practice exams will help you identify your strong areas and pinpoint where you need improvement. By completing these tests under timed conditions, you will build the exam discipline and confidence required to succeed.

This course is updated to stay 100% aligned with the latest ISTQB CT-GenAI v1.0 syllabus (2025 release).

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Coupon Code: CC72233B312F4E5DB648
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Validity: 30 Days
Starts: 10/09/2025 12:00 AM PDT (GMT -7)
Expires: 11/08/2025 11:00 PM PDT (GMT -8)

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Price: $0.00 (Free)
Validity: 5 Days
Starts: 10/18/2025 12:00 AM PDT (GMT -7)
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This CT-GenAI Practice Test Course Includes:

6 full-length practice exams with 40 questions each (240 total)
Detailed explanations for both correct and incorrect answers
Covers all 5 syllabus domains from ISTQB CT-GenAI v1.0
Timed & scored exam simulation (real exam conditions)
Domain weightage alignment with official ISTQB exam guide
Scenario-based, concept-based, and reasoning-style questions
Randomized order to prevent memorization and ensure readiness
Performance reports to identify strengths and areas of improvement
Bonus coupon access to one full test (limited-time offer)
Lifetime updates aligned with new ISTQB CT-GenAI revisions

Exam Details – ISTQB CT-GenAI Certification

Exam Body: ISTQB (International Software Testing Qualifications Board)
Exam Name: ISTQB Certified Tester – Testing with Generative AI (CT-GenAI)
Exam Format: Multiple Choice Questions (MCQs)
Certification Validity: Lifetime (no expiration; no renewal required)
Number of Questions: 40 questions in the real exam
Exam Duration: 60 minutes (75 minutes for non-native English speakers)
Passing Score: 65% (26 out of 40 correct answers)
Question Weightage: 1 point each (some multi-point scenario questions may appear)
Difficulty Level: Specialist-level (Foundation prerequisite required)
Language: English (localized versions may be available)
Exam Availability: Online proctored exam or in test centers (depending on region)
Prerequisite: ISTQB Foundation Level certification

Detailed Syllabus and Topic Weightage

The ISTQB CT-GenAI exam is structured around 5 major syllabus areas. Below is a detailed breakdown along with the approximate exam weightage:

1. Introduction to Generative AI for Software Testing (~12%)

Understand the role and relevance of Generative AI in software testing.
Differentiate Symbolic AI, Machine Learning, Deep Learning, and Generative AI.
Explain the architecture and working principles of Large Language Models (LLMs).
Define core concepts: tokenization, embeddings, context window, and transformer architecture.
Compare foundation models, instruction-tuned, and reasoning LLMs.
Describe multimodal and vision-language models.
Apply Generative AI to requirements analysis, test design, and defect prediction.
Distinguish between AI chatbots, LLM-powered assistants, and test tools.

2. Prompt Engineering for Effective Software Testing (~45%)

Define the structure of an effective prompt: Role, Context, Instruction, Input, Constraints, and Output.
Differentiate zero-shot, one-shot, few-shot, and chain-of-thought prompting.
Explain the concept of meta-prompting and self-improving prompt loops.
Compare system prompts vs. user prompts and their usage in testing contexts.
Use prompting for:
- Test analysis and design
- Automated regression test generation
- Exploratory testing and defect identification
- Test monitoring and control
Evaluate and refine LLM outputs using quality metrics and iterative feedback.
Identify bias and prompt sensitivity issues and apply mitigation techniques.

3. Managing Risks of Generative AI in Software Testing (~20%)

Identify hallucinations, reasoning errors, and biases in Generative AI systems.
Explain the impact of data quality and model limitations on test outcomes.
Describe methods to reduce non-deterministic and inconsistent AI outputs.
Understand security and privacy concerns when using AI for testing.
Evaluate sustainability and energy efficiency in GenAI testing pipelines.
Apply governance, compliance, and AI ethics in testing projects.
Define responsible AI principles and transparency measures.

4. LLM-Powered Test Infrastructure (~13%)

Explain architectural patterns for integrating LLMs into test automation frameworks.
Describe Retrieval-Augmented Generation (RAG) and its application in QA.
Understand fine-tuning, embeddings, and vector database use in AI testing workflows.
Discuss the role of AI agents and multi-agent systems in test execution and reporting.
Implement LLMOps principles for continuous improvement of AI-driven testing systems.
Outline monitoring, logging, and scaling approaches for GenAI testing platforms.

5. Deploying and Integrating Generative AI in Test Organizations (~10%)

Define the organizational roadmap for adopting Generative AI in testing.
Recognize risks of Shadow AI and establish governance controls.
Develop strategies for AI adoption, tool selection, and process integration.
Select appropriate LLMs and small language models (SLMs) based on testing goals.
Plan for upskilling testers in prompt engineering and AI literacy.
Manage change and measure ROI in GenAI-driven test transformation projects.

Learning Outcomes

By the end of this course, learners will be able to:

Explain Generative AI fundamentals and their testing implications.
Design structured prompts to generate effective and reliable test artifacts.
Identify and mitigate risks like hallucinations and data bias in AI testing.
Implement LLMOps and AI infrastructure in modern testing ecosystems.
Develop GenAI testing strategies for enterprise adoption and maturity growth.

Relative Weightage: Chapter 2 (Prompt Engineering) is the most heavily tested, followed by Chapter 3 (Risks).

Practice Test Structure

6 Full-Length Tests
- Each test contains 40 exam-style questions
- Covers all CT-GenAI syllabus domains
Detailed Feedback and Explanations
- Detailed explanation for each correct & incorrect option
- Reinforces learning and avoids repeated mistakes
Randomized Order
- Prevents memorization, ensures real exam readiness
Progress Tracking
- Instant scoring, pass/fail status, weak areas highlighted

Sample Practice Questions (CT-GenAI)

Question 1 (Scenario-based):
A test automation architect is designing an LLM-powered system where test cases generated for microservices need to account for the dependencies and integration points between services, requiring the LLM to understand not just individual service specifications but the broader system architecture (Choose any three).

Options:
A. Provide system architecture diagrams and service dependency mappings as part of the prompt context.
B. Use prompt chaining where service-level tests are generated first, then integration tests are generated using service test outputs as context.
C. Implement RAG to retrieve relevant integration test examples from previous microservices testing projects.
D. Meta-prompting is unnecessary because microservices testing is straightforward and doesn’t require strategic planning.

Answer: A, B, C

Explanation:
A. Provide system architecture diagrams and service dependency mappings as part of the prompt context.
B. Use prompt chaining where service-level tests are generated first, then integration tests are generated using service test outputs as context.
C. Implement RAG to retrieve relevant integration test examples from previous microservices testing projects.
D. Meta-prompting is unnecessary because microservices testing is straightforward and doesn’t require strategic planning.

Domain: Prompt Engineering for Effective Software Testing

Question 2 (Knowledge-based):
What is the primary advantage of using multimodal LLMs for testing complex user interfaces?

Options:
A. They consume less computational resources than text-only models
B. They analyze both visual UI elements and textual specifications simultaneously
C. They only work with voice commands
D. They eliminate the need for test data

Answer: B

Explanation:
A: Correct. Including visual or textual representations of system architecture, service dependencies, communication patterns, and integration points gives the LLM critical context about how services interact, enabling it to generate integration test cases that verify cross-service functionality, identify potential failure points at service boundaries, and suggest contract tests that validate integration assumptions between dependent services.

B: Correct. Prompt chaining enables a structured approach where individual service test cases are generated in initial steps providing foundation for understanding each service’s functionality, then subsequent prompts use these service-level tests as context to generate integration tests that verify interactions between services, check contract compatibility, and validate end-to-end workflows spanning multiple services, creating comprehensive coverage of both component and integration levels.

C: Correct. Retrieval-Augmented Generation can enhance microservices test generation by finding similar architectural patterns from past projects, retrieving integration test examples that addressed comparable service dependency scenarios, providing the LLM with proven testing approaches for common microservices challenges like eventual consistency and distributed transactions, and leveraging organizational knowledge about effective integration testing strategies for service-oriented architectures.

D) Microservices testing actually benefits significantly from meta-prompting that encourages the LLM to first analyze service dependencies, identify integration points requiring testing, consider failure scenarios in distributed systems, plan coverage across different architectural layers, and then generate tests systematically. The distributed and interconnected nature of microservices creates complexity that strategic decomposition through meta-prompting helps address more comprehensively.

Domain: Introduction to Generative AI for Software Testing

Question 3 (Scenario-based):

A DevOps team is integrating LLM-powered test generation into their CI/CD pipeline and needs to determine appropriate strategies for handling situations where the LLM service experiences downtime or rate limiting during critical deployment windows.

Options:
A. Pipeline execution should fail immediately when LLM services are unavailable to maintain quality standards.
B. LLM test generation should only occur in non-production environments to avoid CI/CD reliability issues.
C. Rate limiting indicates the LLM is unsuitable for CI/CD integration and should be removed entirely.
D. Implement fallback mechanisms including cached responses, previously generated test suites, and graceful degradation to maintain pipeline reliability.

Answer: D

Explanation:
A) Failing the entire pipeline due to LLM unavailability creates unnecessary deployment blockers and couples pipeline reliability to external service availability. More resilient architectures implement fallback strategies that maintain pipeline functionality even when AI-assisted features are temporarily unavailable, ensuring critical deployments can proceed while logging degraded functionality for investigation.
B) Restricting LLM usage to non-production environments limits the value of AI-assisted testing by preventing continuous test improvement in production pipelines. With appropriate reliability patterns including fallbacks, caching, and graceful degradation, LLM services can be safely integrated into production CI/CD while maintaining pipeline reliability and enabling ongoing test suite enhancement.
C) Rate limiting is a common cloud service management practice that can be addressed through proper implementation strategies including request optimization, caching, quota management, and architectural patterns that batch test generation outside the critical deployment path. Abandoning LLM integration due to rate limiting ignores effective mitigation approaches and sacrifices valuable capabilities that can be retained through proper engineering.
D) Correct. Robust CI/CD integration with LLM services requires resilience strategies including maintaining caches of previously generated test cases for stable features, implementing fallback to existing test suites when generation fails, setting appropriate timeouts to prevent pipeline delays, providing graceful degradation that logs LLM unavailability without blocking deployments, and establishing monitoring to track service reliability. This architecture balances AI-assisted improvements with operational reliability requirements.

Domain: LLM-Powered Test Infrastructure for Software Testing

Preparation Strategy & Guidance

6 Full-Length Mock Exams: 40 questions each, timed & scored
Study the Exam Blueprint: Focus on high-weightage topics (Prompt Engineering & Risk Management).
Practice Under Exam Conditions: Take 40-question tests in 60 minutes.
Review Mistakes: Understand not just correct answers but why others are wrong.
Master Prompt Engineering: Expect scenario-based questions here.
Target >80% in practice exams, even though 65% is the pass mark.
Continuous Revision: Repeat practice tests until fully confident.
Detailed Explanations: Every question includes rationales for all options.
Timed Simulation: Build focus and real exam pacing.
Randomized Questions: Prevent memorization and improve adaptability.
Performance Tracking: Domain-level analytics to guide your revision.