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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q12-Q17):

NEW QUESTION # 12
What defines a prompt pattern in the context of structured GenAI capability building?

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

Answer: D

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 # 13
Which statement BEST differentiates an LLM-powered test infrastructure from a traditional chatbot system used in testing?

A. It produces scripted conversational responses similar to traditional bots
B. It provides fixed responses from predefined rule sets and scripts
C. It dynamically generates test insights using contextual information
D. It focuses primarily on visual dashboards and user navigation features

Answer: C

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

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

Answer: B

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
You must use GenAI to perform test analysis on a payments module with finalized requirements: (1) generate test conditions, (2) prioritize by risk, (3) check coverage gaps. Which sequence best applies prompt chaining?

A. Generate conditions -> prioritize by risk -> map to requirements to find gaps
B. Generate prioritized conditions in one shot -> verify coverage
C. Detect requirement defects -> generate conditions -> prioritize
D. Prioritize requirements -> generate conditions -> review defects

Answer: A

Explanation:
Prompt Chainingis a technique where a complex task is decomposed into several smaller, sequential steps, where the output of one step serves as the context or input for the next. This is far more reliable than a "one- shot" approach (Option A) because it reduces the cognitive load on the LLM and allows for intermediate verification. In the scenario of test analysis, the most logical and effective chain begins by extracting discrete test conditionsfrom the raw requirements. Once these conditions are established, the next "link" in the chain is toprioritize them based on risk(impact and likelihood), which requires the model to reason specifically about the importance of each condition. The final step is tomap these prioritized conditions back to the original requirementsto identify any "coverage gaps." This systematic flow (Option B) mirrors the professional test analysis process defined in the ISTQB/CT-GenAI standards. By following this sequence, the tester ensures that the AI-generated output is logically derived and thorough, providing a clear "audit trail" from the initial requirement to the final prioritized test suite.

NEW QUESTION # 16
You are using an LLM to assist in analyzing test execution trends to predict potential risks. Which of the following improvements would BEST enhance the LLM's ability to predict risks and provide actionable alerts?

A. Emphasize constraints that focus on deviations that could impact release timelines or quality gates.
B. Specify that the role is a test analyst with expertise in predictive analytics and risk management.
C. Add an instruction to calculate statistical variance and highlight tests that deviate by more than 20% from baseline metrics.
D. Expand the output format to include risk predictions with severity levels, recommended actions, and a timeline for team intervention based on trend analysis.

Answer: D

Explanation:
The effectiveness of an LLM is heavily dependent on the specificity of itsOutput Format. While role definition (Option C) and technical instructions (Option D) are helpful, the most significant "value add" for a test lead is receiving information that is directlyactionable. By expanding the output format to include structuredrisk predictions, severity levels, and recommended actions(Option B), the tester is forcing the LLM to perform a deeper level of analysis. Instead of just "flagging trends," the model must now synthesize the data to determinewhya trend is a risk andwhatthe team should do about it. This aligns with the "Advanced Prompting" section of the CT-GenAI syllabus, which emphasizes using AI for decision support. A structured report that includes a "timeline for intervention" allows the human tester to quickly validate the AI's logic and make informed decisions, transforming the LLM from a simple data summarizer into a strategic predictive tool that actively supports the maintenance of release quality and schedule adherence.

NEW QUESTION # 17
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