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Die Fragenkataloge von ITZert enthalten die Lernmaterialien und Simulationsfragen zur ISQI CT-GenAI Zertifizierungsprüfung. Noch wichtiger bieten wir die originalen CT-GenAI Fragen Und Antworten.
| Section | Objectives |
|---|---|
| Topic 1: Application of GenAI in Software Testing | - Practical use in testing workflows
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| Topic 2: Prompt Engineering for Testing | - Prompt design techniques
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| Topic 3: Organizational Adoption and Governance | - Enterprise GenAI adoption
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| Topic 4: Risk, Quality, and Limitations of GenAI | - Risks in GenAI usage
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| Topic 5: Foundations of Generative AI and LLMs | - Introduction to Generative AI in Software Testing
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Unsere Schulungsunterlagen können Ihre Kenntnisse vor der ISQI CT-GenAI Prüfung testen und auch Ihr Verhalten in einer bestimmten Zeit bewerten. Wir geben Ihnen Anleitung zu Ihrer Note und Schwachpunkt, so dass Sie Ihre Schwäche nachholen können. Die Lernhilfe zur ISQI CT-GenAI Zertifizierungsprüfung von ITZert stellen Ihnen unterschiedliche logische Themen vor. So können Sie nicht nur lernen, sondern auch andere Techiniken und Subjekte kennen lernen. Wir versprechen, dass unsere ISQI CT-GenAI Schlungsunterlagen von der Praxis bewährt werden. ITZert hat genügende Vorbereitung für Ihre Prüfung getroffen. Unsere Fragen sind umfassend und der Preis ist rational.
21. Frage
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?
Antwort: A
Begründung:
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.
22. Frage
What is a hallucination in LLM outputs?
Antwort: D
Begründung:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.
23. Frage
Which technique MOST directly reduces hallucinations by grounding the model in project realities?
Antwort: B
Begründung:
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.
24. Frage
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?
Antwort: B
Begründung:
In the evaluation of GenAI outputs for testing, metrics must align with the specific nature of the task. For anomaly identification, the goal is to correctly identify true issues without an overwhelming number of false positives; therefore,Precisionis the critical metric (the ratio of true anomalies to the total flagged).
Conversely, forpartition testing(identifying valid and invalid input classes), the goal is thoroughness and ensuring no significant category is missed.Recallis the most appropriate metric here, as it measures the model's ability to "call back" or cover all possible relevant partitions from the requirement set. As highlighted in the CT-GenAI syllabus, evaluating AI effectiveness often requires a combination of these model- performance metrics. While "Accuracy" (Option D) provides a general view, it is often misleading in imbalanced testing scenarios (like anomaly detection where anomalies are rare). By using Precision and Recall together, a test organization can quantitatively assess if the AI is both trustworthy in its alerts and comprehensive in its test design coverage.
25. Frage
What is a primary compliance concern related to Shadow AI in organizational test environments?
Antwort: A
Begründung:
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.
26. Frage
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CT-GenAI Zertifizierungsprüfung: https://www.itzert.com/CT-GenAI_valid-braindumps.html
P.S. Kostenlose und neue CT-GenAI Prüfungsfragen sind auf Google Drive freigegeben von ITZert verfügbar: https://drive.google.com/open?id=1GeaMDe7eClDHQD2PT1zzO6GBL4V47YM3