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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q27-Q32):

NEW QUESTION # 27
Your team needs to generate 500 API test cases for a REST API with 50 endpoints. You have documented 10 exemplar test cases that follow your organization's standard format. You want the LLM to generate test cases following the pattern demonstrated in your examples. Which of the following prompting techniques is BEST suited to achieve your goal in this scenario?

A. Few-shot prompting
B. Meta prompting
C. Prompt chaining
D. Zero-shot prompting

Answer: A

Explanation:
Few-shot promptingis the technique of providing a few examples (exemplars) within the prompt to demonstrate the desired task and output format to the LLM. In this scenario, providing 10 existing, high- quality test cases acts as a "pattern" for the model to follow. This is significantly more effective than "Zero- shot prompting" (Option D), where the model is given a task without examples and may deviate from the specific organizational format required (e.g., specific JSON structures or assertion styles). While "Prompt chaining" (Option A) is useful for breaking down complex tasks into sub-tasks, the primary need here is pattern recognition and replication, which is the core strength of Few-shot learning. "Meta prompting" (Option C) involves having the AI write the prompt itself, which is unnecessary when the team already has clear examples. By using Few-shot prompting, the tester "conditions" the model's latent space to prioritize the provided format, ensuring that all 500 generated test cases maintain consistency with the HTTP methods, headers, and assertion logic defined in the exemplars.

NEW QUESTION # 28
The model flags anomalies in logs and also proposes partitions for input validation tests. Which metrics BEST evaluate these two outcomes together?

A. Precision for anomaly identification and recall for coverage of valid/invalid partitions
B. Time efficiency for anomaly detection and accuracy for coverage of valid/invalid partitions
C. Accuracy for anomaly detection and Precision for coverage of valid/invalid partitions
D. Diversity for anomaly identification and precision for partitions

Answer: A

Explanation:
In the evaluation of GenAI outputs for testing, metrics must align with the specific nature of the task. For anomaly identification, the goal is to correctly identify true issues without an overwhelming number of false positives; therefore,Precisionis the critical metric (the ratio of true anomalies to the total flagged).
Conversely, forpartition testing(identifying valid and invalid input classes), the goal is thoroughness and ensuring no significant category is missed.Recallis the most appropriate metric here, as it measures the model's ability to "call back" or cover all possible relevant partitions from the requirement set. As highlighted in the CT-GenAI syllabus, evaluating AI effectiveness often requires a combination of these model- performance metrics. While "Accuracy" (Option D) provides a general view, it is often misleading in imbalanced testing scenarios (like anomaly detection where anomalies are rare). By using Precision and Recall together, a test organization can quantitatively assess if the AI is both trustworthy in its alerts and comprehensive in its test design coverage.

NEW QUESTION # 29
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. Instruction
C. Output format
D. Input data

Answer: D

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 # 30
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

A. Maintaining data privacy and minimizing security risks from external services
B. Maximizing scalability by deploying larger cloud-based LLM clusters
C. Focusing primarily on user experience improvements and response formatting
D. Achieving faster responses by reducing model checkpoints and updates

Answer: A

Explanation:
LLMOps(Large Language Model Operations) is the set of practices used to manage the lifecycle of LLMs in production. When an organization integrates an AI chatbot into its test processes, the primary operational concern ismaintaining data privacy and minimizing security risks, especially if using third-party APIs.
Unlike traditional software, LLMs are "black boxes" that process every piece of data sent to them. A core LLMOps responsibility is ensuring that any "Prompt Data" (code, requirements, or logs) is not used by the provider to train their public models and that the communication channels are fully secured. While scalability (Option A) and latency (Option C) are important technical metrics, they are secondary to the catastrophic legal and reputational risk of a data breach. LLMOps in a testing context involves implementing data masking tools, monitoring for "Prompt Injection" attacks, and managing the "Grounding" data in vector databases to ensure it remains current and protected. This ensures the AI remains a safe and reliable asset within the enterprise testing ecosystem, rather than a liability for the organization's intellectual property.

NEW QUESTION # 31
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. Add a step to review the change log for syntax errors before analysis.
B. Include references to version control systems like Git in the constraints.
C. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
D. Specify that the role is a test architect specializing in CI/CD pipelines.

Answer: C

Explanation:
The most effective way to improve an LLM's performance on complex tasks likeimpact analysisis to provide a detailed, multi-stepInstructionorChain-of-Thoughtstructure. Option D is the best improvement because it breaks the "impact analysis" task into logical sub-tasks: mapping changes to modules, identifying related test cases, and prioritizing them based on risk and complexity. This structured approach guides the LLM through the "reasoning" steps a human expert would take, significantly reducing the likelihood of a superficial or incorrect analysis. While specifying a specialized role (Option B) or adding technical references (Option A) can help set the tone, they do not provide the model with the logical framework required to execute the task accurately. By explicitly defining theprocessthe LLM should follow, the tester ensures that the model evaluates the "depth" of the change rather than just listing files. This results in a more robust and actionable regression test suite, which is the primary goal of impact analysis in a modern software development lifecycle.

NEW QUESTION # 32
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