ISQI CT-GenAI Zertifizierung, CT-GenAI Prüfung

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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Tools for Testing Generative AI20%- Testing Tools Overview
  • 1. Selecting appropriate tools for specific testing needs
  • 2. Categories of GenAI testing tools
- Using Tools for Common Testing Activities
  • 1. Prompt testing tools
  • 2. Model evaluation tools
  • 3. Security testing tools
  • 4. Simulation and monitoring tools
Topic 2: Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Inconsistent responses across runs
  • 2. Input sensitivity (prompt brittleness)
  • 3. Inappropriate output for the context
  • 4. Offensive, harmful, or biased content
  • 5. Dependency on external components
  • 6. Incorrect or fabricated outputs (hallucinations)
- Testing Challenges for Generative AI
  • 1. Regulatory and compliance considerations
  • 2. Ethical testing concerns
  • 3. Test oracle problem
  • 4. Non-deterministic output behavior
  • 5. Complexity of the AI component
  • 6. Subjectivity of quality assessment
  • 7. Coverage challenges
Topic 3: Testing Activities for Generative AI30%- Model and Output Evaluation
  • 1. Checkpoint testing
  • 2. Output correctness and quality assessment
  • 3. Human evaluation methods
  • 4. Automated evaluation methods
  • 5. Metamorphic testing
- Prompt-Based Testing
  • 1. Test case design using prompts
  • 2. Prompt engineering basics
  • 3. Test data creation with GenAI
- Traceability and Documentation
  • 1. Documentation requirements for AI testing
  • 2. Test coverage of AI model components
- Requirements-Based Testing
  • 1. Functional requirements for AI-based systems
  • 2. AI-related quality requirements
  • 3. Non-functional requirements for AI-based systems
Topic 4: Fundamentals of Generative AI20%- AI Development Lifecycle
  • 1. Model training and fine-tuning
  • 2. Data collection, preparation, and curation
  • 3. Deployment and monitoring
  • 4. Evaluation
- Generative AI Concepts
  • 1. Emergent capabilities and limitations
  • 2. Model types (Base, Instruction-tuned, RAG)
  • 3. Training data and context windows
  • 4. Hallucinations
  • 5. Alignment and guardrails
  • 6. AI model behavior
- AI Terminology
  • 1. Deep Learning
  • 2. Machine Learning (ML)
  • 3. Transformer architecture
  • 4. Large Language Models (LLMs)
  • 5. Generative AI (GenAI)
  • 6. Artificial Intelligence (AI)
  • 7. Tokens and prompts

>> ISQI CT-GenAI Zertifizierung <<

Hilfsreiche Prüfungsunterlagen verwirklicht Ihren Wunsch nach der Zertifikat der ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

Seit der Gründung der ZertFragen wird unser System immer verbessert ---- Immer reichlicher Test-Bank, gesicherter Zahlungsgarantie und besserer Kundendienst. Heute sind die ISQI CT-GenAI Prüfungsunterlagen schon von zahlreichen Kunden anerkennt worden. Nach Ihrem Kauf hört unser Kundendienst nicht aus. Wir werden Ihnen die Informationen über die Aktualisierungssituation der ISQI CT-GenAI rechtzeitig. Wir sind auch verantwortlich für Ihre Verlust. Falls Sie nicht wunschgemäß die ISQI CT-GenAI Prüfung bestehen, geben wir alle Ihre für ISQI CT-GenAI bezahlte Gebühren zurück.

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q30-Q35):

30. Frage
What is a hallucination in LLM outputs?

Antwort: B

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.


31. Frage
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

Antwort: C

Begründung:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.


32. Frage
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

Antwort: C

Begründung:
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.


33. 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.


34. Frage
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

Antwort: D

Begründung:
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.


35. Frage
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