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| Section | Objectives |
|---|---|
| Topic 1: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Topic 2: Organizational Adoption and Governance | - Enterprise GenAI adoption
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| Topic 3: Application of GenAI in Software Testing | - Practical use in testing workflows
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| Topic 4: Prompt Engineering for Testing | - Prompt design techniques
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| Topic 5: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
|
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NEW QUESTION # 41
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?
Answer: D
Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.
NEW QUESTION # 42
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?
Answer: B
Explanation:
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.
NEW QUESTION # 43
A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?
Answer: C
Explanation:
In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
"Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
"Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.
NEW QUESTION # 44
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?
Answer: B
Explanation:
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.
NEW QUESTION # 45
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 # 46
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