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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q25-Q30):

NEW QUESTION # 25
What is a hallucination in LLM outputs?

A. Generation of factually incorrect content for the task
B. A systematic preference learned from data
C. A transient network failure during inference
D. A logical mistake in multi-step deduction

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 # 26
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 # 27
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 prioritized conditions in one shot -> verify coverage
B. Prioritize requirements -> generate conditions -> review defects
C. Detect requirement defects -> generate conditions -> prioritize
D. Generate conditions -> prioritize by risk -> map to requirements to find gaps

Answer: D

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 # 28
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

A. Strict GDPR compliance eliminates all privacy risk
B. Using GenAI without regulatory compliance can lead to legal exposure
C. Some GenAI tools may store/process data without explicit consent
D. GenAI outputs can accidentally reveal sensitive information present in inputs

Answer: A

Explanation:
The statement that "Strict GDPR compliance eliminates all privacy risk" isincorrectbecause compliance is a legal and procedural framework, not a foolproof technical shield against all possible risks. Even within a GDPR-compliant environment, risks such as "model inversion" attacks, accidental data leakage through
"membership inference," or the unintentional generation of Sensitive Personally Identifiable Information (SPII) can still occur. Data privacy in GenAI is complex because LLMs function by processing and sometimes retaining patterns from the data they are fed. As noted in the CT-GenAI syllabus, some tools may process data in ways that are not fully transparent (Option A), and outputs can inadvertently include snippets of sensitive data used during the prompting or training phase (Option B). Furthermore, failing to adhere to regulations like GDPR or the EU AI Act certainly leads to legal and financial exposure (Option D). Therefore, while compliance frameworks significantly mitigate risk, they do not "eliminate" it; a robust GenAI strategy requires ongoing technical controls, data masking, and human oversight to manage residual privacy threats effectively.

NEW QUESTION # 29
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 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.
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 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.
D. 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.

Answer: D

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 # 30
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