CT-GenAI Reliable Exam Syllabus - Trustable ISQI CT-GenAI Related Content: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0

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ISQI CT-GenAI Exam Syllabus Topics:

Section	Weight	Objectives
Tools for Testing Generative AI	20%	- Using Tools for Common Testing Activities 1. Simulation and monitoring tools 2. Model evaluation tools 3. Security testing tools 4. Prompt testing tools - Testing Tools Overview 1. Selecting appropriate tools for specific testing needs 2. Categories of GenAI testing tools
Testing Activities for Generative AI	30%	- Model and Output Evaluation 1. Automated evaluation methods 2. Output correctness and quality assessment 3. Metamorphic testing 4. Human evaluation methods 5. Checkpoint testing - Requirements-Based Testing 1. Functional requirements for AI-based systems 2. Non-functional requirements for AI-based systems 3. AI-related quality requirements - Prompt-Based Testing 1. Test data creation with GenAI 2. Prompt engineering basics 3. Test case design using prompts - Traceability and Documentation 1. Test coverage of AI model components 2. Documentation requirements for AI testing
Fundamentals of Generative AI	20%	- Generative AI Concepts 1. AI model behavior 2. Emergent capabilities and limitations 3. Training data and context windows 4. Hallucinations 5. Alignment and guardrails 6. Model types (Base, Instruction-tuned, RAG) - AI Development Lifecycle 1. Model training and fine-tuning 2. Data collection, preparation, and curation 3. Deployment and monitoring 4. Evaluation - AI Terminology 1. Machine Learning (ML) 2. Transformer architecture 3. Generative AI (GenAI) 4. Tokens and prompts 5. Deep Learning 6. Artificial Intelligence (AI) 7. Large Language Models (LLMs)
Risks and Testing Challenges for Generative AI	30%	- Testing Challenges for Generative AI 1. Regulatory and compliance considerations 2. Subjectivity of quality assessment 3. Ethical testing concerns 4. Non-deterministic output behavior 5. Test oracle problem 6. Complexity of the AI component 7. Coverage challenges - 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 (Q14-Q19):

NEW QUESTION # 14
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

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

Answer: B

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 # 15
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. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
B. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.
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: 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 # 16
Which setting can reduce variability by narrowing the sampling distribution during inference?

A. Lowering temperature
B. Increasing temperature
C. Increasing learning rate
D. Using a larger context window

Answer: A

Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.

NEW QUESTION # 17
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. Constraints
B. Input data
C. Instruction
D. Output format

Answer: B

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 # 18
You are tasked with applying structured prompting to perform impact analysis on recent code changes. Which of the following improvements would BEST align the prompt with structured prompt engineering best practices for comprehensive impact analysis?

A. Include references to version control systems like Git in the constraints.
B. Specify that the role is a test architect specializing in CI/CD pipelines.
C. Add a step to review the change log for syntax errors before analysis.
D. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Answer: D

NEW QUESTION # 19
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