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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q22-Q27):

NEW QUESTION # 22
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A. i -> iv -> iii -> ii
B. iv -> iii -> i -> ii
C. iii -> iv -> i -> ii
D. iv -> i -> iii -> ii

Answer: D

Explanation:
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.

NEW QUESTION # 23
What is a key data-related aspect when defining a GenAI strategy for testing?

A. Prioritize accurate and relevant input data secured through defined quality procedures
B. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
C. Aggregate data from all available organizational repositories without filtration
D. Use only auto-generated synthetic data to avoid dependency on enterprise repositories

Answer: A

Explanation:
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.

NEW QUESTION # 24
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

A. Writing low-level device drivers
B. Mastering prompt engineering
C. Designing custom CPU instructions
D. Configuring network routers

Answer: B

Explanation:
As Generative AI becomes integrated into the software testing lifecycle, the role of the tester shifts from manual authoring to the "orchestration" of AI models. Mastering prompt engineering is the primary competency required to effectively steer LLMs. Prompt engineering involves the deliberate design of inputs- incorporating roles, context, instructions, and constraints-to elicit the most accurate and "on-policy" outputs from the model. In a testing context, "on-policy" refers to testware that adheres to organizational standards, security protocols, and specific project requirements. While technical skills like network configuration or low- level programming (Options B, C, and D) are valuable in specific engineering domains, they do not directly influence the communicative interface between the human and the AI. A tester proficient in prompt engineering can utilize techniques like "Chain-of-Thought" or "Few-shot prompting" to ensure the LLM understands the nuances of a test plan, thereby reducing hallucinations and ensuring the generated test cases are actionable, relevant, and compliant with the project's quality gates.

NEW QUESTION # 25
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

A. Ability to respond to prompts without explicit user instructions
B. Use of a conversational tone and improved response personalization
C. Ability to trigger automated actions beyond conversation
D. Reliance on predefined templates to generate short, factual answers

Answer: C

NEW QUESTION # 26
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?

A. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
B. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
C. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
D. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.

Answer: A

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 # 27
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