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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 認定 CT-GenAI 試験問題 (Q22-Q27):

質問 # 22
What does an embedding represent in an LLM?

正解：A

解説：
Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
"feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.


質問 # 23
Which option BEST differentiates the three prompting techniques?

正解：A

解説：
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.


質問 # 24
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

正解：B


質問 # 25
Which setting can reduce variability by narrowing the sampling distribution during inference?

正解：D

解説：
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.


質問 # 26
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

正解：A

解説：
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.


質問 # 27
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