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

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The ISQI CT-GenAI practice questions come with three easy-to-use and install formats. The certification for the ISQI CT-GenAI exam is a valuable, well-recognized professional credential. You can develop your skills and become a recognized specialist with the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 CT-GenAI Certification in addition to learning about new technology requirements.

ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q26-Q31):

NEW QUESTION # 26
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 mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
B. Add a step to review the change log for syntax errors before analysis.
C. Include references to version control systems like Git in the constraints.
D. Specify that the role is a test architect specializing in CI/CD pipelines.

Answer: A

NEW QUESTION # 27
What defines a prompt pattern in the context of structured GenAI capability building?

A. Maintaining static documentation repositories without real-time prompt standardization processes
B. Using ad hoc prompts without reference to previously proven structures or examples
C. Applying a reusable and structured template that guides GenAI models toward consistent outputs
D. Treating prompts as access credentials or compliance records rather than functional templates

Answer: C

Explanation:
In the context of structured Generative AI capability building, a prompt pattern is a formalized method of interaction that ensures repeatability and reliability. Much like software design patterns, prompt patterns provide a reusable and structured template designed to guide Large Language Models (LLMs) toward producing specific, high-quality, and consistent outputs. Without these patterns, testers often rely on "zero- shot" or ad hoc prompting, which frequently leads to non-deterministic results that are difficult to validate in a professional testing lifecycle. By adopting prompt patterns, organizations can standardize how requirements are translated into test cases or how code is analyzed for defects. This standardization is critical for scaling GenAI across a team, as it allows for the creation of a "prompt library" where successful structures-such as Persona-based, Few-shot, or Chain-of-Thought patterns-are documented and reused. This approach moves the use of GenAI from a trial-and-error activity to a disciplined engineering practice, ensuring that the model understands the specific context, constraints, and expected output formats required for rigorous software testing tasks.

NEW QUESTION # 28
Which standard specifies requirements for managing AI systems within an organization, supporting consistent GenAI use in testing?

A. NIST AI RMF 1.0
B. ISO/IEC 23053:2022
C. ISO/IEC 42001:2023
D. EU AI Act

Answer: C

Explanation:
ISO/IEC 42001:2023is the international standard for an AI Management System (AIMS). It is designed to help organizations develop, provide, or use AI systems responsibly by providing a certifiable framework of requirements and controls. In a software testing context, this standard is vital for establishing governance, ensuring that GenAI tools are used consistently and ethically across the lifecycle.NIST AI RMF 1.0(Option B) is a highly respected framework, but it is a set of voluntary guidelines for managing risk, not a
"requirement standard" for a management system.ISO/IEC 23053:2022(Option C) provides a general framework for AI using machine learning but lacks the comprehensive "management system" scope found in
42001. Finally, theEU AI Act(Option D) is a regulation (law), not a technical standard. For a test organization looking to align its GenAI strategy with international best practices and achieve formal certification, ISO/IEC
42001 is the definitive standard to follow, as it covers the organizational processes, data handling, and risk management necessary for high-quality AI operations.

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 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.
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 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.
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
What is a key data-related aspect when defining a GenAI strategy for testing?

A. Neglect legacy data sources as they provide limited immediate relevance to testing tasks
B. Aggregate data from all available organizational repositories without filtration
C. Use only auto-generated synthetic data to avoid dependency on enterprise repositories
D. Prioritize accurate and relevant input data secured through defined quality procedures

Answer: D

Explanation:
A successful Generative AI strategy for testing is heavily dependent on the quality of the data used for grounding (RAG) and prompting. The principle of "Garbage In, Garbage Out" is magnified with LLMs; therefore, a key strategic pillar is the prioritization of accurate, relevant, and high-quality input data. This involves establishing defined quality procedures to ensure that the requirements, codebases, and historical defect logs fed into the model are "clean" and representative of the current system state. Strategy must avoid the "unfiltered" approach (Option C), as including contradictory or obsolete data can lead to hallucinations or irrelevant test cases. While synthetic data (Option D) is a powerful tool for privacy, it cannot entirely replace the nuanced reality found in secured enterprise data. Furthermore, legacy data (Option A) often contains valuable insights for regression testing. Consequently, the strategy should focus on building a robust data pipeline that ensures only verified, contextually appropriate information is utilized, thereby increasing the reliability of AI-generated testware and ensuring it aligns with the organization's quality standards.

NEW QUESTION # 31
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