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ISQI CT-GenAI Exam Syllabus Topics:

Section	Objectives
Topic 1: Application of GenAI in Software Testing	- Practical use in testing workflows 1. Regression suite optimization 2. Test case generation using LLMs 3. Defect report analysis and summarization 4. Test data generation and augmentation
Topic 2: Foundations of Generative AI and LLMs	- Introduction to Generative AI in Software Testing 1. Difference between chatbots and LLM-based test tools 2. LLM basics, tokenization, context window, multimodal models
Topic 3: Organizational Adoption and Governance	- Enterprise GenAI adoption 1. Policy, ethics, and compliance considerations 2. LLMOps and governance models 3. Integration into CI/CD pipelines
Topic 4: Risk, Quality, and Limitations of GenAI	- Risks in GenAI usage 1. Data privacy and security concerns 2. Hallucinations and reasoning errors 3. Bias and fairness issues 4. Environmental and energy considerations
Topic 5: Prompt Engineering for Testing	- Prompt design techniques 1. Zero-shot, one-shot, few-shot prompting 2. Prompt chaining and meta prompting 3. Structuring prompts for test case generation

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q10-Q15):

NEW QUESTION # 10
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. | 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 # 11
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. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
C. Add a step to review the change log for syntax errors before analysis.
D. Specify that the role is a test architect specializing in CI/CD pipelines.

Answer: B

NEW QUESTION # 12
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. Prompt chaining
B. Meta prompting
C. Few-shot prompting
D. Zero-shot prompting

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 # 13
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. Instruction
B. Output format
C. Constraints
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 # 14
Which statement about data privacy risks in GenAI-assisted testing is INCORRECT?

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

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
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