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| Section | Objectives |
|---|---|
| Topic 1: Application of GenAI in Software Testing | - Practical use in testing workflows
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| Topic 2: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
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| Topic 3: Prompt Engineering for Testing | - Prompt design techniques
|
| Topic 4: Organizational Adoption and Governance | - Enterprise GenAI adoption
|
| Topic 5: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
|
>> Valid CT-GenAI Exam Syllabus <<
The ISQI CT-GenAI certification exam is a crucial part of career development in the tech sector. Cracking the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) exam strengthens your chances of landing high-paying jobs and promotions. Yet, preparing for the CT-GenAI Exam can be challenging, and many working applicants struggle to find CT-GenAI practice test questions they require to be successful in their pursuit.
NEW QUESTION # 26
What is a hallucination in LLM outputs?
Answer: C
Explanation:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.
NEW QUESTION # 27
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?
Answer: D
Explanation:
Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.
NEW QUESTION # 28
Which statement about fine-tuning for test tasks is INCORRECT?
Answer: C
Explanation:
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.
NEW QUESTION # 29
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?
Answer: C
NEW QUESTION # 30
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.
Answer: C
Explanation:
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.
NEW QUESTION # 31
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