Latest ISQI CT-GenAI of exam practice questions and answers

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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q36-Q41):

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 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.
B. 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.
C. 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.
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: C

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
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

A. Generate conditions -> prioritize by risk -> map to requirements to find gaps
B. Prioritize requirements -> generate conditions -> review defects
C. Detect requirement defects -> generate conditions -> prioritize
D. Generate prioritized conditions in one shot -> verify coverage

Answer: A

Explanation:
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.

NEW QUESTION # 38
A prompt begins: "You are a senior test manager responsible for risk-based test planning on a payments platform." Which component is this?

A. Instruction
B. Context
C. Constraints
D. Role

Answer: D

Explanation:
In structured prompt engineering, theRolecomponent (also known as a Persona) is used to set the perspective, expertise, and tone of the LLM's response. By assigning the role of a "senior test manager," the tester instructs the model to adopt the specific domain knowledge, vocabulary, and professional standards associated with that position. This technique is highly effective because LLMs are trained on vast datasets containing diverse professional documents; invoking a specific persona helps the model narrow its "latent space" to retrieve information relevant to that specific field. For instance, a senior test manager persona will prioritize risk management, resource allocation, and high-level strategy, whereas a "junior developer" persona might focus more on syntax and local unit tests. WhileContext(Option B) provides the background of the project andInstruction(Option A) defines the specific task to be performed, theRoleserves as the foundation for how those instructions are interpreted. This ensures the generated testware aligns with the expected professional seniority and organizational maturity required for high-stakes environments like a payments platform.

NEW QUESTION # 39
Which statement about fine-tuning for test tasks is INCORRECT?

A. It adapts a pre-trained model to a domain using task-specific data
B. It can be applied to smaller SLMs to improve task performance with lower compute
C. It enhances relevance to organizational terminology and formats
D. It replaces the model's general knowledge entirely and prevents overfitting

Answer: D

Explanation:
The statement that fine-tuning "replaces the model's general knowledge entirely" isincorrect. Fine-tuning is a process of "incremental learning" where a pre-trained model (which already possesses vast general knowledge) is further trained on a smaller, domain-specific dataset-such as an organization's internal API documentation or historical test scripts. The goal is to adjust the model's internal weights so that it becomes more proficient in a specific area (Option A) and adheres better to local terminology and formatting standards (Option C). It doesnoterase the foundational language capabilities of the model. Furthermore, fine-tuning is a common strategy for Small Language Models (SLMs) to allow them to punch above their weight class in specific tasks while remaining computationally efficient (Option D). However, if done poorly, fine-tuning can actuallycauseoverfitting (where the model becomes too rigid and loses its ability to generalize), rather than preventing it. Therefore, fine-tuning should be viewed as a "specialization" layer rather than a total replacement of the model's base intelligence.

NEW QUESTION # 40
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

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

Answer: B

NEW QUESTION # 41
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