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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q35-Q40):

NEW QUESTION # 35
A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

A. Input data
B. Instruction
C. Output format
D. Constraints

Answer: A

Explanation:
In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
"Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
"Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.

NEW QUESTION # 36
Consider applying the meta-prompting technique to generate automated test scripts for API testing. You need to test a REST API endpoint that processes user registration with validation rules. Which one of the following prompts is BEST suited to this task?

A. Role: Act as a test automation engineer with API testing experience. | Context: You are verifying user registration that enforces field and format validation. | Instruction: Generate pytest scripts using requests for both positive (valid) and negative (invalid email, weak password, missing fields) cases. | Input Data: POST /api/register with validation rules for email and password length. | Constraints:
Include fixtures, clear assertions, and naming consistent with pytest. | Output Format: Return complete Python test files.
B. Role: Act as a software engineer. | Context: You are testing registration logic. | Instruction: Create Python scripts to verify endpoint behavior. | Input Data: POST /api/register with test users. | Constraints: Add checks for status codes. | Output Format: Deliver functional scripts.
C. Role: Act as an automation tester. | Context: You are validating an API endpoint. | Instruction: Generate Python test scripts that send POST requests and validate responses. | Input Data: User credentials. | Constraints: Include basic scenarios with asserts. | Output Format: Provide organized scripts.
D. Role: Act as a test automation engineer. | Context: You are creating tests for a registration endpoint. | Instruction: Generate Python test scripts using pytest covering both valid and invalid inputs. | Input Data: POST /api/register with email and password. | Constraints: Follow pytest structure. | Output Format: Provide scripts.

Answer: A

Explanation:
Option A is the superior choice because it strictly adheres to thestructured prompting patternrecommended in the CT-GenAI syllabus. This pattern divides the prompt into six distinct components:Role, Context, Instruction, Input Data, Constraints, and Output Format.By specifying theRole(Senior Test Automation Engineer), the model accesses relevant technical knowledge. TheInstructionis specific about using pytest and the requests library, and it explicitly lists both positive and negative scenarios. Most importantly, the Constraintssection provides the necessary "guardrails" for the code structure, such as the use of fixtures and clear assertions. Options B, C, and D are increasingly vague and fail to provide the model with the necessary technical boundaries to produce "production-ready" testware. Structured prompting reduces the "probabilistic drift" of the model, ensuring the output is not just functional code, but a script that follows industry-standard testing patterns (like modularity and clean naming conventions), making it directly usable within a CI/CD pipeline.

NEW QUESTION # 37
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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

Answer: A

NEW QUESTION # 38
What does an embedding represent in an LLM?

A. Tokens grouped into context windows
B. Logical rules for reasoning
C. A set of test cases for validation
D. Numerical vectors capturing semantic relationships

Answer: D

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 # 39
Which competency MOST helps testers steer LLMs to produce useful, on-policy testware?

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

Answer: D

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 # 40
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