Real ISQI CT-GenAI Dumps–Attempt the Exam in the Optimal Way

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q18-Q23):

NEW QUESTION # 18
What are the three key phases in adopting GenAI in a test organization?

Answer: A

Explanation:
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


NEW QUESTION # 19
An attacker sends extremely long prompts to overflow context so the model leaks snippets from its training data. Which attack vector is this?

Answer: D

Explanation:
This scenario describes a specialized form ofData Exfiltration(specifically targeting the model's internal
"weights" or training memory). While data exfiltration usually refers to stealing data from a database, in the context of LLMs, it can also refer to techniques that force the model to "reveal" sensitive information it was trained on or data that exists within its current context window. By using long, repetitive, or specifically
"crafted" prompts to overwhelm the model's normal attention mechanisms or safety filters, an attacker may cause the model to output verbatim snippets of proprietary information, PII, or internal documentation that should have remained confidential. This is different fromRequest Manipulation(Option D), which aims to change the model's behavior, orData Poisoning(Option A), which happens during training. In testing, this risk is high when models are fine-tuned on private company repositories. Testers must be aware that if a model is accessible to unauthorized users, those users might use adversarial prompting techniques to extract sensitive code or business logic through these types of data leakage attacks.


NEW QUESTION # 20
Which statement BEST describes vision-language models (VLMs)?

Answer: C

Explanation:
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.


NEW QUESTION # 21
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: A


NEW QUESTION # 22
Which setting can reduce variability by narrowing the sampling distribution during inference?

Answer: A

Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.


NEW QUESTION # 23
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