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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which statement BEST contrasts interaction style and scope?

A. Chatbots require API integration; LLM apps do not.
B. Both are identical aside from UI theme.
C. Chatbots enable conversational interactions; LLM apps provide capabilities for defined test tasks.
D. Chatbots enforce fixed workflows; LLM apps support free-form exploration beneficial for software testing

Answer: C

Explanation:
It is important to distinguish between a general-purposeChatbotand a specializedLLM applicationwithin a testing framework. A chatbot is primarily designed for multi-turn, conversational interactions where the user can ask questions and receive answers in a natural language format. While useful for general queries, it often lacks the specialized workflow integration needed for rigorous testing. Conversely,LLM applications(or
"LLM-powered tools") are built with a specific "scope" in mind, such as automated test generation, code analysis, or requirement mapping. These applications often use the LLM as an underlying engine but surround it with specific UI components, data connectors (like RAG), and fixed task-oriented prompts to achieve a defined testing outcome. While chatbots are "free-form," LLM apps are "capability-driven." This distinction is key for organizations defining a GenAI strategy; simply providing a chatbot to testers is rarely sufficient.
Instead, organizations should develop or adopt LLM applications that integrate directly into the CI/CD pipeline and provide structured, actionable test artifacts that support defined quality engineering tasks.

NEW QUESTION # 14
Which statement BEST describes vision-language models (VLMs)?

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

Answer: C

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 # 15
What is a hallucination in LLM outputs?

A. A systematic preference learned from data
B. A transient network failure during inference
C. A logical mistake in multi-step deduction
D. Generation of factually incorrect content for the task

Answer: D

Explanation:
A hallucination refers to a phenomenon where a Large Language Model generates text that is grammatically correct and seemingly plausible but is factually incorrect or unsupported by the provided context or real-world data. In the context of software testing, this is a critical limitation. For example, an LLM might generate a test case for a software feature that does not exist or cite a non-existent API parameter. These errors occur because LLMs are probabilistic engines designed to predict the "most likely" next token rather than "reasoning" from a set of verified facts. They do not have a built-in "truth" mechanism. While a logical mistake (Option B) is a failure in reasoning and a systematic preference (Option D) describes bias, a hallucination is specifically about the fabrication of information. Testers must be particularly vigilant regarding hallucinations, as they can lead to "false confidence" in test coverage or the creation of invalid bug reports. Mitigations include grounding the model with Retrieval-Augmented Generation (RAG) and implementing rigorous "human-in-the- loop" verification of all AI-generated test artifacts.

NEW QUESTION # 16
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 produces scripted conversational responses similar to traditional bots
D. It focuses primarily on visual dashboards and user navigation features

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 # 17
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

A. Transitioning from manual execution to complete automation with no human oversight
B. Shifting from test execution toward reviewing, refining, and validating AI-generated testware
C. Moving from black-box exploratory testing toward exclusively performing code-based white-box checks
D. Replacing existing test coverage validation with automated summary reports generated by AI

Answer: B

Explanation:
As Generative AI is integrated into the testing lifecycle, the role of the human tester undergoes a significant shift from "author" to "orchestrator and reviewer." In traditional testing, a significant portion of a tester's time is spent manually drafting test cases, scripts, and documentation. With GenAI, these artifacts can be generated in seconds. Consequently, the tester's responsibility shifts towardreviewing, refining, and validatingthe AI- generated testware to ensure accuracy, relevance, and compliance with project goals. This "Human-in-the- Loop" (HITL) approach is critical because LLMs are prone to hallucinations and may lack the deep domain context of a human expert. Testers must apply their critical thinking to verify that the AI-generated scripts actually cover the necessary edge cases and do not contain logical errors. This evolution does not mean the end of human oversight (Option B) or a move exclusively to white-box testing (Option C). Instead, it elevates the tester to a higher-level analytical role, focusing on quality strategy and the final verification of AI outputs rather than the repetitive task of initial content creation.

NEW QUESTION # 18
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