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The modern ISQI world is changing its dynamics at a fast pace. With the ISQI CT-GenAI certification, you can learn these changes and stay updated all the time. There are other countless ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) certification exam benefits that you can gain after passing the exam. The prominent ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) certification exam benefits are validation of skills, more career opportunity, salary increment, and the opportunity to become a member of the ISQI community.

ISQI CT-GenAI Exam Syllabus Topics:

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

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                                  ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q20-Q25):

                                  NEW QUESTION # 20
                                  The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

                                  Answer: D

                                  Explanation:
                                  In the evaluation of GenAI outputs for testing, metrics must align with the specific nature of the task. For anomaly identification, the goal is to correctly identify true issues without an overwhelming number of false positives; therefore,Precisionis the critical metric (the ratio of true anomalies to the total flagged).
                                  Conversely, forpartition testing(identifying valid and invalid input classes), the goal is thoroughness and ensuring no significant category is missed.Recallis the most appropriate metric here, as it measures the model's ability to "call back" or cover all possible relevant partitions from the requirement set. As highlighted in the CT-GenAI syllabus, evaluating AI effectiveness often requires a combination of these model- performance metrics. While "Accuracy" (Option D) provides a general view, it is often misleading in imbalanced testing scenarios (like anomaly detection where anomalies are rare). By using Precision and Recall together, a test organization can quantitatively assess if the AI is both trustworthy in its alerts and comprehensive in its test design coverage.


                                  NEW QUESTION # 21
                                  What is a key data-related aspect when defining a GenAI strategy for testing?

                                  Answer: C

                                  Explanation:
                                  A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.


                                  NEW QUESTION # 22
                                  Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

                                  Answer: B

                                  Explanation:
                                  A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.


                                  NEW QUESTION # 23
                                  You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

                                  Answer: A

                                  Explanation:
                                  Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.


                                  NEW QUESTION # 24
                                  How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

                                  Answer: B

                                  Explanation:
                                  As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.


                                  NEW QUESTION # 25
                                  ......

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