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CT-GenAI Latest Questions | CT-GenAI Training Online

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q24-Q29):

NEW QUESTION # 24
Which of the following is NOT a valid form of LLM-driven test data generation?

A. Creating combinatorial data (e.g., pairwise)
B. Generating synthetic datasets
C. Setting boundary values
D. Creating production database backups

Answer: D

Explanation:
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.

NEW QUESTION # 25
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

A. Select broad-coverage models offering diverse functionalities for various test scenarios
B. Select models with maximum vendor visibility and strong online presence to ensure reliability
C. Select LLMs aligned to measurable test outcomes, compatible with current infrastructure
D. Select open-source models prioritizing creativity over compliance or performance consistency

Answer: C

Explanation:
A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.

NEW QUESTION # 26
Which setting can reduce variability by narrowing the sampling distribution during inference?

A. Lowering temperature
B. Increasing learning rate
C. Using a larger context window
D. Increasing temperature

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 # 27
What are the three key phases in adopting GenAI in a test organization?

A. Prototype; pilot; decommission
B. Planning; execution; sign-off
C. Training; certification; outsourcing
D. Discovery; initiation and usage definition; utilization and iteration

Answer: D

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 # 28
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

A. Rely on generic examples only
B. Use longer temperature settings
C. Provide detailed context
D. Randomize prompts each run

Answer: C

Explanation:
Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
"project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.

NEW QUESTION # 29
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