CT-GenAI Questions & Answers & CT-GenAI Study Guide & CT-GenAI Exam Preparation

ISQI CT-GenAI : ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

Exam Code: CT-GenAI

Exam Name: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

Updated: Aug 16, 2026

Q & A: 42 Questions and Answers

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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: Application of GenAI in Software Testing- Practical use in testing workflows
  • 1. Regression suite optimization
    • 2. Test data generation and augmentation
      • 3. Defect report analysis and summarization
        • 4. Test case generation using LLMs
          Topic 2: Prompt Engineering for Testing- Prompt design techniques
          • 1. Structuring prompts for test case generation
            • 2. Zero-shot, one-shot, few-shot prompting
              • 3. Prompt chaining and meta prompting
                Topic 3: Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
                • 1. Bias and fairness issues
                  • 2. Hallucinations and reasoning errors
                    • 3. Data privacy and security concerns
                      • 4. Environmental and energy considerations
                        Topic 4: Foundations of Generative AI and LLMs- Introduction to Generative AI in Software Testing
                        • 1. Difference between chatbots and LLM-based test tools
                          • 2. LLM basics, tokenization, context window, multimodal models
                            Topic 5: Organizational Adoption and Governance- Enterprise GenAI adoption
                            • 1. Integration into CI/CD pipelines
                              • 2. Policy, ethics, and compliance considerations
                                • 3. LLMOps and governance models

                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions:

                                  1. What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

                                  A) Reliance on predefined templates to generate short, factual answers
                                  B) Ability to respond to prompts without explicit user instructions
                                  C) Ability to trigger automated actions beyond conversation
                                  D) Use of a conversational tone and improved response personalization


                                  2. Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

                                  A) Zero-shot prompting
                                  B) Few-shot prompting
                                  C) Prompt chaining
                                  D) Meta prompting


                                  3. Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

                                  A) ISO/IEC 42001:2023
                                  B) NIST AI RMF 1.0
                                  C) EU AI Act
                                  D) ISO/IEC 23053:2022


                                  4. The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

                                  A) Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
                                  B) Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
                                  C) Diversity for anomaly identification and precision for partitions
                                  D) Precision for anomaly identification and recall for coverage of valid/invalid partitions


                                  5. Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

                                  A) The location of the data center determines model bias and accuracy levels
                                  B) The duration of user sessions primarily affects latency but not power efficiency
                                  C) The number of tokens processed directly determines the carbon intensity of each query
                                  D) The type of cloud platform affects processing speed but not total energy draw


                                  Solutions:

                                  Question # 1
                                  Answer: C
                                  Question # 2
                                  Answer: B
                                  Question # 3
                                  Answer: A
                                  Question # 4
                                  Answer: D
                                  Question # 5
                                  Answer: C

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