Free PDF Quiz 2026 Latest ISQI CT-GenAI: ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Valid Test Pdf

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ISQI CT-GenAI Exam Syllabus Topics:

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

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CT-GenAI Exam Reference, CT-GenAI Quiz

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q33-Q38):

NEW QUESTION # 33
A team notices vague, inconsistent LLM outputs for the same story for two different prompts. Which technique BEST helps choose the stronger wording among two prompt versions using predefined metrics?

A. Iterative prompt modification
B. Output analysis
C. Integrating user feedback
D. A/B testing of prompts

Answer: D

Explanation:
A/B testing, also known as split testing, is a systematic empirical method used to compare two versions of a prompt (Version A and Version B) to determine which one performs better based on predefined evaluation metrics. In the realm of LLMs, where outputs can be stochastic (probabilistic), A/B testing is essential for mitigating inconsistency. When a team encounters vague or varying results for a user story, simply modifying the prompt iteratively (Option B) may improve the result but does not provide a statistical or objective basis for why one version is superior. Byrunning A/B tests, testers can evaluate prompts against specific KPIs such as accuracy, relevance, format adherence, or the absence of hallucinations. This process involves sending the same input data through both prompt versions multiple times and scoring the outputs. The version that consistently yields the "stronger wording" or more precise testware is then selected as the production standard. This data-driven approach is a cornerstone of prompt engineering in professional environments, ensuring that the most effective linguistic structures are utilized to maximize the model's performance and reliability.

NEW QUESTION # 34
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 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.
B. 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.
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 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.

Answer: B

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 # 35
What does an embedding represent in an LLM?

A. Numerical vectors capturing semantic relationships
B. Logical rules for reasoning
C. Tokens grouped into context windows
D. A set of test cases for validation

Answer: A

Explanation:
Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
"feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.

NEW QUESTION # 36
Which option BEST differentiates the three prompting techniques?

A. Chaining = give examples; Few-shot = break tasks; Meta = manual edits only
B. Few-shot = no examples; Chaining = single prompt; Meta = disable iteration
C. Meta = step decomposition; Chaining = zero-shot only; Few-shot = manual optimization
D. Few-shot = examples; Chaining = multi-step prompts; Meta = model helps draft/refine prompts

Answer: D

Explanation:
Differentiating between prompting techniques is essential for a tester to select the right tool for the task.Few- shot promptingis characterized by providing the model with a few examples of inputs and desired outputs, allowing it to learn the pattern and format.Prompt Chaininginvolves breaking a complex task into a sequence of smaller, interconnected prompts, where the output of one step becomes the input for the next (e.g., first extract requirements, then generate test cases from those requirements).Meta-promptingis a more advanced technique where the user asks the LLM to help design, write, or refine the prompt itself-essentially using the AI as a "prompt engineer" to optimize the instructions. Option D correctly identifies these core characteristics.
Options A, B, and C contain fundamental mischaracterizations: for instance, Few-shotrequiresexamples (contradicting A), and Chaining is theoppositeof a single prompt (contradicting A). Mastering these distinctions allows testers to move from simple "chatting" to sophisticated AI orchestration that can handle complex, multi-stage testing workflows with high reliability.

NEW QUESTION # 37
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. Include mapping code changes to affected modules, identifying test cases, prioritizing by risk level and change complexity
D. Add a step to review the change log for syntax errors before analysis.

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 # 38
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