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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q41-Q46):

NEW QUESTION # 41
What is a primary compliance concern related to Shadow AI in organizational test environments?

Answer: B

Explanation:
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


NEW QUESTION # 42
Which option BEST differentiates the three prompting techniques?

Answer: D

Explanation:
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.


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
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 # 45
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 # 46
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