CT-GenAI測試引擎，CT-GenAI考古題介紹

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ISQI CT-GenAI Exam Syllabus Topics:

SectionObjectives
Topic 1: Organizational Adoption and Governance- Enterprise GenAI adoption
  • 1. Integration into CI/CD pipelines
    • 2. Policy, ethics, and compliance considerations
      • 3. LLMOps and governance models
        Topic 2: Application of GenAI in Software Testing- Practical use in testing workflows
        • 1. Test data generation and augmentation
          • 2. Regression suite optimization
            • 3. Defect report analysis and summarization
              • 4. Test case generation using LLMs
                Topic 3: 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 4: 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
                          Topic 5: Risk, Quality, and Limitations of GenAI- Risks in GenAI usage
                          • 1. Bias and fairness issues
                            • 2. Environmental and energy considerations
                              • 3. Data privacy and security concerns
                                • 4. Hallucinations and reasoning errors

                                  >> CT-GenAI測試引擎 <<

                                  CT-GenAI考古題介紹 & CT-GenAI考題資源

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                                  最新的 AI Testing CT-GenAI 免費考試真題 (Q14-Q19):

                                  問題 #14
                                  What is a key data-related aspect when defining a GenAI strategy for testing?

                                  答案：C

                                  解題說明：
                                  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.


                                  問題 #15
                                  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?

                                  答案：A

                                  解題說明：
                                  This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
                                  This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
                                  "human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.


                                  問題 #16
                                  Which of the following is NOT a valid form of LLM-driven test data generation?

                                  答案：C

                                  解題說明：
                                  Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
                                  "generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.


                                  問題 #17
                                  Which AI approach requires feature engineering and structured data preparation?

                                  答案：C

                                  解題說明：
                                  Classical Machine Learning(which includes algorithms like Random Forests, Support Vector Machines, and Linear Regression) is characterized by its reliance onFeature Engineering. This is the process where human experts manually select, extract, and transform raw data into a set of "features" or variables that the algorithm can process. For instance, in a classical ML model predicting software defects, a tester might have to manually define features like "lines of code changed" or "number of previous bugs." In contrast,Deep Learningand its subset,Generative AI(Options B and D), utilize "Representation Learning." This means the multi-layered neural networks automatically identify and extract the relevant features from raw, often unstructured data (like text or images) without explicit human instruction.Symbolic AI(Option A) is based on hard-coded logical rules rather than data-driven learning. Understanding this distinction is fundamental for testers, as it determines the level of data preparation required: Classical ML requires high human effort in data structuring, while GenAI requires high effort in prompt engineering and grounding.


                                  問題 #18
                                  In the context of software testing, which statements (i-v) about foundation, instruction-tuned, and reasoning LLMs are CORRECT?
                                  i. Foundation LLMs are best suited for broad exploratory ideation when test requirements are underspecified.
                                  ii. Instruction-tuned LLMs are strongest at adhering to fixed test case formats (e.g., Gherkin) from clear prompts.
                                  iii. Reasoning LLMs are strongest at multi-step root-cause analysis across logs, defects, and requirements.
                                  iv. Foundation LLMs are optimal for strict policy compliance and template conformance.
                                  v. Instruction-tuned LLMs can follow stepwise reasoning without any additional training or prompting.

                                  答案：B

                                  解題說明：
                                  Understanding the hierarchy of LLM types is vital for selecting the right tool for specific testing tasks.
                                  Foundation LLMsare trained on massive datasets to predict the next token; they excel at broad, creative
                                  "ideation" (Statement i) but often struggle with following specific instructions or constraints (making Statement iv incorrect).Instruction-tuned LLMshave undergone additional training (Fine-tuning) to follow explicit commands and templates. They are highly effective at structured tasks like converting requirements into Gherkin feature files (Statement ii).Reasoning LLMs(or those utilizing specialized prompting like Chain- of-Thought) are designed to handle complex, multi-stage logic. This makes them the superior choice for diagnostic tasks like root-cause analysis, where the model must synthesize information across logs and requirements to find a defect's origin (Statement iii). Statement v is incorrect because while instruction-tuned models are capable, complex "stepwise reasoning" usually requires specific prompting techniques or the inherent logic of specialized reasoning models. Therefore, the combination of i, ii, and iii represents the correct alignment of model capability to testing functionality.


                                  問題 #19
                                  ......

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