ISTQB Testing - GenAI CT-GenAI Practice Exams 240 Questions

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 has been completely rebuilt and validated against the latest official ISTQB CT-GenAI syllabus.

It now provides 100% Learning Objective coverage with corrected K-level distribution (K1/K2/K3) exactly aligned to the real exam blueprint. 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 answersCovers all syllabus chapters with full Learning Objective traceability and domain weightage alignment as per the official exam structure. Clear domain identification provided for every questionTimed & scored exam simulation (real exam conditions)Domain weightage alignment with official ISTQB exam guideScenario-based, concept-based, and reasoning-style questionsRandomized order to prevent memorization and ensure readinessPerformance reports to identify strengths and areas of improvementBonus coupon access to one full test (limited-time offer)Lifetime updates aligned with new ISTQB CT-GenAI revisionsExam Details – ISTQB CT-GenAI CertificationExam 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 examExam Duration: 60 minutes (75 minutes for non-native English speakers)Passing Score: 65% (26 out of 40 correct answers)Question Weightage: Correct 1-point and 2-point distribution strictly aligned with the official CT-GenAI scoring model.

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 certificationDetailed Syllabus and Topic WeightageThe 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 (~17.

5%)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 (~27. 5%)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 designAutomated regression test generationExploratory testing and defect identificationTest monitoring and controlEvaluate 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 (~25%)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 (~12.

5%)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 (~17. 5%)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 OutcomesBy 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 Structure6 Full-Length TestsEach test contains 40 exam-style questionsCovers all CT-GenAI syllabus domainsDetailed Feedback and ExplanationsDetailed explanation for each correct & incorrect optionReinforces learning and avoids repeated mistakesRandomized OrderPrevents memorization, ensures real exam readinessProgress TrackingInstant scoring, pass/fail status, weak areas highlightedSample Practice Questions (CT-GenAI)Question 1 (Scenario-based):A senior tester must generate test cases for a requirements document that contains ambiguous acceptance criteria and conflicting business rules across three related features.

The tester needs the LLM to first clarify the conflicts before generating test cases. Which prompting approach is MOST appropriate for this scenario?. Options:A.

Zero-shot prompting, providing the full requirements document and requesting complete test case output in a single prompt. B. Few-shot prompting, supplying three example test cases from a previous project as context before requesting new test cases.

C. Prompt chaining, first prompting the LLM to identify and resolve ambiguities, then using that output to generate test cases. D.

Role prompting alone, instructing the LLM to act as a senior test analyst and generate test cases directly from the requirements. Answer: CExplanation:A. This is incorrect because zero-shot prompting submits a single prompt without intermediate clarification steps, making it unsuitable when ambiguities must be resolved before test case generation can produce accurate results.

A single prompt cannot first resolve conflicts and then generate test cases as discrete, dependent operations. The ambiguity in the requirements requires an approach that sequences clarification before generation. B.

This is incorrect because few-shot prompting provides examples to guide output format and style but does not incorporate a conflict resolution step before test case generation. Providing prior examples does not address the need to first identify and resolve ambiguities in the current requirements. This technique improves output quality but does not sequence conflict clarification as a prerequisite step.

C. This is correct because prompt chaining sequences the conflict clarification step as a prerequisite output that informs the subsequent test case generation prompt, directly addressing the need to resolve ambiguities before generating accurate test cases, as per reference 2. 2.

5. This approach decomposes the task into dependent stages that match the tester's stated requirement. The sequential dependency between clarification and generation is the defining advantage of this technique for this scenario.

D. This is incorrect because role prompting establishes a persona but does not create a structured sequence that ensures ambiguities are resolved before test cases are generated. Without a chaining mechanism, the model may generate test cases based on misinterpreted conflicting rules despite the assigned role.

Role prompting affects response style rather than enforcing a task dependency sequence. Question 2 (Knowledge-based):Which term BEST describes the behavior of a generative AI system that produces a confident and fluent response containing information that is factually incorrect or entirely fabricated? Options:A.

BiasB. Reasoning errorC. HallucinationD.

Non-determinismAnswer: CExplanation:A: This is incorrect because bias refers to systematic skewing of outputs based on imbalanced training data or model assumptions, producing consistently skewed rather than entirely fabricated content. Bias does not describe the production of confident, fluent, but factually incorrect or invented information. B: This is incorrect because a reasoning error refers to a logical flaw in the model's inferential process, such as incorrect deductions or invalid conclusions from valid premises.

While related, it does not specifically describe the generation of confident, fluent, fabricated content. C: This is correct because hallucination describes the generative AI behavior of producing responses that appear confident and fluent but contain factually incorrect or entirely fabricated content, as per reference section 3. 1.

1. This term specifically identifies the failure mode where the model generates plausible-sounding but false information. Other terms describe related but distinct failure modes.

D) This is incorrect because non-determinism refers to the variability in LLM outputs when given identical inputs, producing different results across runs. It describes output variability, not the specific failure of generating confidently stated but factually incorrect or fabricated content. Question 3 (Scenario-based):Which TWO of the following correctly identify sources of training data bias in generative AI systems as recognized in the context of software testing?

(Select TWO)Options:A. Insufficient computational resources allocated to the model during the inference phase of operation. B.

Historical data reflecting past human decisions that encode systemic inequities or outdated practices. C. Underrepresentation of certain demographic groups or technical domains within the datasets used to train the model.

D. Use of cloud-based deployment environments for serving the trained model to end users. Answer: B, CExplanation:A) This is incorrect because computational resource allocation during inference is a performance and infrastructure concern, not a source of training data bias.

Bias originates in the composition and characteristics of training datasets, not in the hardware or compute capacity available at inference time. B) This is correct because training on historical human-generated content perpetuates existing biases embedded in that data, as per reference section 3. 1.

1. When LLMs are trained on records reflecting past systemic inequities or obsolete methodologies, those biases are encoded in the model's output tendencies. This is a primary recognized source of bias in generative AI systems relevant to testing applications.