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ISQI CT-GenAI Exam Syllabus Topics:

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

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

NEW QUESTION # 37
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?

Answer: B

Explanation:
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.


NEW QUESTION # 38
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?

Answer: D

Explanation:
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.


NEW QUESTION # 39
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Answer: A


NEW QUESTION # 40
What is a hallucination in LLM outputs?

Answer: A

Explanation:
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.


NEW QUESTION # 41
What does an embedding represent in an LLM?

Answer: A

Explanation:
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.


NEW QUESTION # 42
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