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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q37-Q42):

NEW QUESTION # 37
Which concept refers to breaking text into smaller units for processing by LLMs?

Answer: A

Explanation:
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.


NEW QUESTION # 38
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

Answer: A


NEW QUESTION # 39
What are the three key phases in adopting GenAI in a test organization?

Answer: B

Explanation:
According to the strategic frameworks for AI adoption (as detailed in the CT-GenAI and related ISO/IEC
42001 standards), the journey toward organizational AI maturity follows three primary phases. TheDiscovery phase involves identifying potential use cases, assessing current technical readiness, and understanding the legal/risk landscape. TheInitiation and Usage Definitionphase is where the organization sets the "ground rules"-defining which tools are approved, establishing system prompts, creating prompt libraries, and training the staff on prompt engineering. This phase transitions the AI from a novelty into a structured capability. Finally, theUtilization and Iterationphase is the ongoing process where GenAI is used in daily testing activities, and its outputs are constantly monitored, measured, and improved through feedback loops.
This ensures the strategy remains dynamic and adapts to new model capabilities or changing project requirements. Options B, C, and D represent standard project management or IT lifecycles but do not capture the specific "learning and refinement" nature required for successful Generative AI integration in a testing department.


NEW QUESTION # 40
Who typically defines the system prompt in a testing workflow?

Answer: D

Explanation:
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.


NEW QUESTION # 41
Which option BEST differentiates the three prompting techniques?

Answer: A

Explanation:
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.


NEW QUESTION # 42
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