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ISQI CT-GenAI Exam Syllabus Topics:

SectionWeightObjectives
Risks and Testing Challenges for Generative AI30%- Quality Risks Specific to Generative AI
  • 1. Dependency on external components
  • 2. Inconsistent responses across runs
  • 3. Incorrect or fabricated outputs (hallucinations)
  • 4. Offensive, harmful, or biased content
  • 5. Input sensitivity (prompt brittleness)
  • 6. Inappropriate output for the context
- Testing Challenges for Generative AI
  • 1. Ethical testing concerns
  • 2. Complexity of the AI component
  • 3. Subjectivity of quality assessment
  • 4. Test oracle problem
  • 5. Coverage challenges
  • 6. Non-deterministic output behavior
  • 7. Regulatory and compliance considerations
Testing Activities for Generative AI30%- Prompt-Based Testing
  • 1. Prompt engineering basics
  • 2. Test case design using prompts
  • 3. Test data creation with GenAI
- Model and Output Evaluation
  • 1. Output correctness and quality assessment
  • 2. Human evaluation methods
  • 3. Metamorphic testing
  • 4. Automated evaluation methods
  • 5. Checkpoint 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
Tools for Testing Generative AI20%- Using Tools for Common Testing Activities
  • 1. Model evaluation tools
  • 2. Prompt testing tools
  • 3. Security testing tools
  • 4. Simulation and monitoring tools
- Testing Tools Overview
  • 1. Selecting appropriate tools for specific testing needs
  • 2. Categories of GenAI testing tools
Fundamentals of Generative AI20%- AI Terminology
  • 1. Generative AI (GenAI)
  • 2. Tokens and prompts
  • 3. Large Language Models (LLMs)
  • 4. Machine Learning (ML)
  • 5. Transformer architecture
  • 6. Deep Learning
  • 7. Artificial Intelligence (AI)
- AI Development Lifecycle
  • 1. Data collection, preparation, and curation
  • 2. Evaluation
  • 3. Model training and fine-tuning
  • 4. Deployment and monitoring
- Generative AI Concepts
  • 1. Training data and context windows
  • 2. Model types (Base, Instruction-tuned, RAG)
  • 3. Hallucinations
  • 4. Emergent capabilities and limitations
  • 5. Alignment and guardrails
  • 6. AI model behavior

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

NEW QUESTION # 26
What distinguishes an LLM-powered agent from a basic AI chatbot in test processes?

Answer: C

Explanation:
While a basic chatbot is primarily designed for textual interaction and information retrieval, anLLM- powered agent(or AI Agent) is characterized by itsagency-the ability to use tools and trigger actions in the external world. In a software testing context, an agent does not just "talk" about testing; it can actually perform testing tasks. For example, an agent could be given the goal to "verify the login module," and it would independently decide to call an API, generate a test script, execute it against a test environment, and then analyze the results to report a bug in Jira. This ability totrigger automated actions(Option C) through
"function calling" or tool integration is what makes agents far more powerful than simple conversational interfaces (Option D). Agents can reason about "how" to achieve a goal, selecting the appropriate tools (like Selenium, Postman, or specialized internal utilities) to complete the task. This moves the AI from being a passive advisor to an active participant in the test automation ecosystem, requiring testers to focus more on goal definition and result validation.


NEW QUESTION # 27
Which factor MOST influences the overall energy consumption of a Generative AI model used in software testing tasks?

Answer: D

Explanation:
The environmental impact and sustainability of AI are increasingly important considerations in software engineering. The overall energy consumption of an LLM during inference (when the model is actually being used by a tester) is most directly influenced by thenumber of tokens processed. Every token generated or analyzed requires a massive amount of floating-point operations within the GPU clusters of a data center.
Therefore, the "length" of the input prompt and the "length" of the AI's response are the primary drivers of the power draw and, consequently, the carbon intensity of the query. This is a crucial concept for "Green AI" initiatives in testing; more efficient prompting-such as avoiding unnecessarily verbose context or limiting output lengths-can lead to more sustainable testing practices. While data center location (Option B) affects thetypeof energy used (renewable vs. fossil fuel), it does not determine the model's accuracy. Similarly, while cloud platforms (Option D) and session durations (Option C) play roles in operational logistics, the mathematical workload tied to token count remains the fundamental unit of energy expenditure in Generative AI.


NEW QUESTION # 28
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?

Answer: C


NEW QUESTION # 29
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: C

Explanation:
A mature GenAI strategy for software testing must move beyond "hype" and focus on tangible value and operational feasibility. Selecting an LLM based onmeasurable test outcomes(such as reduction in test design time, increase in defect detection, or script accuracy) ensures that the AI investment directly supports the organization's Quality Assurance goals. Furthermore, the model must becompatible with current infrastructure. This includes considerations for data security (on-prem vs. cloud), API integration capabilities, and cost-per-token efficiency. While vendor visibility (Option A) can be a factor, it is not a guarantee of task-specific performance. Prioritizing creativity over compliance (Option B) is highly risky for testing, where precision and policy adherence are paramount. Similarly, while broad functionality (Option C) is useful, it often results in "jack-of-all-trades" models that may not perform as well as specialized or instruction-tuned models on specific testing tasks. Strategic alignment requires a balance between model performance, organizational security requirements, and clear KPIs.


NEW QUESTION # 30
What BEST protects sensitive test data at rest and in transit?

Answer: A

Explanation:
Data security is a paramount concern when using GenAI in testing, as test environments often contain sensitive business logic or PII (Personally Identifiable Information). To protect this data "at rest" (stored in databases or vector stores) and "in transit" (being sent to the LLM), a combination of technical controls is required.Role-Based Access Control (RBAC)is a fundamental security pillar that ensures only authorized individuals or services can access specific datasets or trigger GenAI workflows. This prevents unauthorized users from feeding sensitive enterprise data into public AI models. While encryption (omitted in Option A as an alternative to obfuscation) and TLS (falsely suggested to be disabled in Option C) are essential technical layers for protecting data in transit, RBAC provides the organizational "gatekeeping" necessary to manage who can interact with the AI system. In a professional GenAI strategy, testers must ensure that the tools they use adhere to strict access policies, ensuring that the "Input Data" used for prompting remains within the secured organizational boundary and is not leaked to unauthorized entities or public training sets.


NEW QUESTION # 31
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