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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

A. The duration of user sessions primarily affects latency but not power efficiency
B. The location of the data center determines model bias and accuracy levels
C. The type of cloud platform affects processing speed but not total energy draw
D. The number of tokens processed directly determines the carbon intensity of each query

Answer: D

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 # 14
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

A. Select models with maximum vendor visibility and strong online presence to ensure reliability
B. Select broad-coverage models offering diverse functionalities for various test scenarios
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 # 15
What defines a prompt pattern in the context of structured GenAI capability building?

A. Treating prompts as access credentials or compliance records rather than functional templates
B. Applying a reusable and structured template that guides GenAI models toward consistent outputs
C. Using ad hoc prompts without reference to previously proven structures or examples
D. Maintaining static documentation repositories without real-time prompt standardization processes

Answer: B

Explanation:
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.

NEW QUESTION # 16
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

A. Replacing existing test coverage validation with automated summary reports generated by AI
B. Shifting from test execution toward reviewing, refining, and validating AI-generated testware
C. Transitioning from manual execution to complete automation with no human oversight
D. Moving from black-box exploratory testing toward exclusively performing code-based white-box checks

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 # 17
Which of the following is NOT a valid form of LLM-driven test data generation?

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

Answer: C

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 # 18
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