CT-GenAI測試題庫 & CT-GenAI考古题推薦

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ISQI CT-GenAI Exam Syllabus Topics:

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

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最新的 AI Testing CT-GenAI 免費考試真題 (Q22-Q27):

問題 #22
Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

答案：A

解題說明：
Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.


問題 #23
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

答案：D


問題 #24
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

答案：D

解題說明：
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.


問題 #25
Who typically defines the system prompt in a testing workflow?

答案：B

解題說明：
In professional Generative AI applications, thesystem prompt(sometimes called the system message) is the foundational set of instructions that defines the AI's persona, boundaries, and overall behavior. In a testing workflow, this is typically defined by atester or test engineerwho is configuring the AI assistant for a specific project. Unlike the user prompt, which changes with every interaction, the system prompt remains relatively static and acts as a "guardrail" to ensure the model stays in its role (e.g., "You are an expert in ISO
26262 automotive testing standards"). By defining the system prompt, the tester ensures that the model consistently uses specific terminology, adheres to data privacy constraints, and formats its output according to the team's requirements. While end users (Option B) provide the task-specific input, they do not usually have the permissions or technical need to alter the underlying system-level instructions. Similarly, while CI servers (Option C) might trigger the prompt, they do not "define" the human-centric logic contained within it.
Properly crafting the system prompt is a core part of setting up an AI-augmented test environment.


問題 #26
Which setting can reduce variability by narrowing the sampling distribution during inference?

答案：D

解題說明：
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.


問題 #27
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