CT-GenAI Fragen Beantworten, CT-GenAI Vorbereitung

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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Risks and Testing Challenges for Generative AI	30%	- Quality Risks Specific to Generative AI 1. Dependency on external components 2. Inappropriate output for the context 3. Inconsistent responses across runs 4. Offensive, harmful, or biased content 5. Incorrect or fabricated outputs (hallucinations) 6. Input sensitivity (prompt brittleness) - Testing Challenges for Generative AI 1. Coverage challenges 2. Non-deterministic output behavior 3. Ethical testing concerns 4. Test oracle problem 5. Regulatory and compliance considerations 6. Subjectivity of quality assessment 7. Complexity of the AI component
Testing Activities for Generative AI	30%	- Requirements-Based Testing 1. Non-functional requirements for AI-based systems 2. Functional requirements for AI-based systems 3. AI-related quality requirements - Model and Output Evaluation 1. Human evaluation methods 2. Automated evaluation methods 3. Checkpoint testing 4. Output correctness and quality assessment 5. Metamorphic testing - Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing - Prompt-Based Testing 1. Test data creation with GenAI 2. Prompt engineering basics 3. Test case design using prompts
Tools for Testing Generative AI	20%	- Testing Tools Overview 1. Categories of GenAI testing tools 2. Selecting appropriate tools for specific testing needs - Using Tools for Common Testing Activities 1. Security testing tools 2. Prompt testing tools 3. Model evaluation tools 4. Simulation and monitoring tools
Fundamentals of Generative AI	20%	- AI Development Lifecycle 1. Deployment and monitoring 2. Model training and fine-tuning 3. Data collection, preparation, and curation 4. Evaluation - AI Terminology 1. Generative AI (GenAI) 2. Tokens and prompts 3. Machine Learning (ML) 4. Large Language Models (LLMs) 5. Artificial Intelligence (AI) 6. Deep Learning 7. Transformer architecture - Generative AI Concepts 1. Hallucinations 2. Model types (Base, Instruction-tuned, RAG) 3. AI model behavior 4. Emergent capabilities and limitations 5. Alignment and guardrails 6. Training data and context windows

>> CT-GenAI Fragen Beantworten <<

CT-GenAI Vorbereitung & CT-GenAI Echte Fragen

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Prüfungsfragen mit Lösungen (Q14-Q19):

14. Frage
Which statement about fine-tuning for test tasks is INCORRECT?

A. It can be applied to smaller SLMs to improve task performance with lower compute
B. It enhances relevance to organizational terminology and formats
C. It adapts a pre-trained model to a domain using task-specific data
D. It replaces the model's general knowledge entirely and prevents overfitting

Antwort: D

Begründung:
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.

15. Frage
Which concept refers to breaking text into smaller units for processing by LLMs?

A. Transformer
B. Context Window
C. Embeddings
D. Tokenization

Antwort: D

Begründung:
Tokenizationis the foundational process by which an LLM breaks down raw text into smaller, manageable units called "tokens." These tokens can represent individual words, parts of words (sub-words), or even punctuation marks. This is a critical step because LLMs do not "read" words like humans do; they process numerical representations of these tokens. The way text is tokenized directly impacts the model's efficiency and its ability to understand complex technical terminology used in software testing. For example, a rare technical term might be broken into several sub-word tokens. This process is closely linked to theContext Window(Option C), which is the maximum number of tokens a model can "remember" or process at one time. WhileEmbeddings(Option B) are the numerical vectors that represent the meaning of these tokens, and theTransformer(Option A) is the underlying architecture that processes them, tokenization is the specific mechanism for initial text decomposition. Understanding tokenization is vital for testers when managing long requirement documents to ensure they do not exceed the model's limits.

16. Frage
Which statement BEST describes vision-language models (VLMs)?

A. VLMs are a subset of multimodal LLMs integrating visual and textual information.
B. VLMs are a superset of multimodal LLMs.
C. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
D. VLMs process audio and video but not images.

Antwort: A

Begründung:
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.

17. Frage
Which setting can reduce variability by narrowing the sampling distribution during inference?

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

Antwort: C

Begründung:
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.

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

A. It provides fixed responses from predefined rule sets and scripts
B. It focuses primarily on visual dashboards and user navigation features
C. It produces scripted conversational responses similar to traditional bots
D. It dynamically generates test insights using contextual information

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.

19. Frage
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