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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q29-Q34):

NEW QUESTION # 29
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. Zero-shot prompting
B. Meta prompting
C. Few-shot prompting
D. Prompt chaining

Answer: C

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 # 30
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. Specify that the role is a test architect specializing in CI/CD pipelines.
D. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity

Answer: D

NEW QUESTION # 31
An LLM prioritizes tests using likelihood X impact but ranks a trivial tooltip change above a payment failure.
What defect does this MOST LIKELY show?

A. Hallucination
B. No defect; this is acceptable
C. Dataset bias toward UI features
D. Reasoning error in risk calculation logic

Answer: D

Explanation:
This scenario describes a failure in the model's ability to apply logical weight to specific domain concepts, specifically in the context of Risk-Based Testing (RBT). When an LLM ranks a low-impact UI element (a tooltip) higher than a critical functional failure (payment processing), it demonstrates a "Reasoning error in risk calculation logic." While LLMs can follow formulas like $Risk = Likelihood \times Impact$, they may lack the deep semantic understanding of "Impact" within a specific business domain unless explicitly guided.
This is not necessarily a hallucination (Option C), as the model isn't necessarily inventing facts, but rather misapplying the logic of prioritization. It is also distinct from dataset bias (Option D), which would involve a systematic skewing across all outputs. In professional testing, this type of error highlights the necessity of
"human-in-the-loop" verification. Testers must review AI-generated prioritizations to ensure that the logical deductions align with the actual business risk and technical criticality of the features being tested.

NEW QUESTION # 32
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 # 33
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

A. It dynamically generates test insights using contextual information
B. It provides fixed responses from predefined rule sets and scripts
C. It focuses primarily on visual dashboards and user navigation features
D. It produces scripted conversational responses similar to traditional bots

Answer: A

Explanation:
The primary differentiator between an LLM-powered test infrastructure and a traditional chatbot is the move from "deterministic" to "probabilistic" logic. Traditional chatbots (Option D) rely on "if-then" logic, decision trees, and predefined scripts. They can only respond to queries that match specific keywords or patterns mapped in their database. In contrast, an LLM-powered infrastructure utilizes the generative capabilities of Large Language Models to synthesize and create new content based on context. This allows it todynamically generate test insights(Option A)-such as predicting potential regression risks based on unstructured code diffs or drafting test cases for a brand-new feature described in natural language. While traditional bots provide fixed, scripted responses (Option B), LLMs can "reason" through multi-step testing problems and provide nuanced explanations. This contextual awareness is powered by the model's training on vast amounts of technical documentation, enabling it to assist in exploratory testing and complex analysis that traditional, rule-based systems simply cannot handle.

NEW QUESTION # 34
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