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| Section | Weight | Objectives |
|---|---|---|
| Deploying and Integrating GenAI in Test Organisations | 15% | - Strategy, governance, and adoption roadmap - Roles, skills, and team readiness - Measuring value and continuous improvement |
| Introduction to Generative AI for Software Testing | 15% | - Capabilities and limitations relevant to testing - Use cases across the testing lifecycle - Core concepts: Generative AI, LLMs, foundation models |
| Prompt Engineering for Effective Software Testing | 35% | - Iterative refinement and evaluation of prompts - Prompt patterns for test design, data generation, automation - Principles and structure of effective prompts |
| Managing Risks of Generative AI in Software Testing | 25% | - Hallucinations, bias, inaccuracy, and consistency risks - Data privacy, security, and compliance concerns - Validation, verification, and mitigation strategies |
| LLM-Powered Test Infrastructure | 10% | - RAG, fine-tuning, and model adaptation - Architecture and deployment considerations - AI agents and integration with test tools |
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NEW QUESTION # 26
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?
Answer: D
Explanation:
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.
NEW QUESTION # 27
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?
Answer: B
Explanation:
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.
NEW QUESTION # 28
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 # 29
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: A
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 # 30
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?
Answer: A
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 # 31
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