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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q18-Q23):

NEW QUESTION # 18
When an organization uses an AI chatbot for testing, what is the PRIMARY LLMOps concern?

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

Answer: D

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 # 19
Which of the following is NOT a valid form of LLM-driven test data generation?

A. Creating production database backups
B. Generating synthetic datasets
C. Setting boundary values
D. Creating combinatorial data (e.g., pairwise)

Answer: A

Explanation:
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.

NEW QUESTION # 20
You must generate test cases for a new payments rule. The system includes API specifications stored in a vector database and prior tests in a relational database. Which of the following sequences BEST represents the correct order for applying a Retrieval-Augmented Generation (RAG) workflow?
i. Retrieve semantically similar specification chunks from the vector database ii. Feed both retrieved datasets as context for the LLM to generate new test cases iii. Retrieve relevant historical cases from the relational database iv. Submit a focused query describing the new test requirement

A. iv -> iii -> i -> ii
B. i -> iv -> iii -> ii
C. iii -> iv -> i -> ii
D. iv -> i -> iii -> ii

Answer: D

Explanation:
A Retrieval-Augmented Generation (RAG) workflow is designed to "ground" an LLM's output in specific, verifiable data. The logical flow begins with an initial input or "focused query" (Step iv) that defines the tester's goal-in this case, generating cases for a new payments rule. The system then uses this query to perform a semantic search in avector database(Step i) to find the most relevant "chunks" of the new API specification. Following this, the system retrieves complementary data from therelational database(Step iii), such as historical test cases that might provide structural patterns or regression context. Finally, all the retrieved information-the new specs and the historical context-is bundled together and "fed" into the LLM as part of an augmented prompt (Step ii). This ensures the LLM doesn't hallucinate rules but instead synthesizes the new requirements with established organizational testing standards. Following the order in Option B ensures that the model is provided with the most relevant and logically organized context prior to generating the final testware.

NEW QUESTION # 21
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 # 22
Which statement BEST describes vision-language models (VLMs)?

A. VLMs are a superset of multimodal LLMs.
B. VLMs process audio and video but not images.
C. VLMs are unrelated to multimodal LLMs and focus only on UI automation.
D. VLMs are a subset of multimodal LLMs integrating visual and textual information.

Answer: D

Explanation:
Vision-Language Models (VLMs)represent a specialized subset of multimodal Large Language Models.
Their defining characteristic is the ability to process, understand, and reason across both textual and visual modalities simultaneously. In the field of software testing, VLMs are revolutionary because they allow the AI to "see" a User Interface (UI). A tester can provide a screenshot of a web page alongside a natural language prompt, and the VLM can identify UI elements, detect visual regressions, or even validate that the visual layout matches a design specification. They are not a "superset" (Option C) of multimodal AI, but rather a specific implementation of it focused on the intersection of sight and language. Unlike traditional OCR or pixel-comparison tools used in legacy UI automation (Option B), VLMs understand thecontextof what they see-for instance, identifying a "broken" button icon that a human would recognize but a rule-based script might miss. This integration of visual and textual data is what makes them a vital component of modern, AI- augmented Quality Assurance strategies.

NEW QUESTION # 23
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