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ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q23-Q28):

NEW QUESTION # 23
Which consideration BEST aligns LLM choice with organizational goals in a GenAI testing strategy?

Answer: D

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 # 24
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

Answer: D

Explanation:
This scenario describes a form ofRequest Manipulation, specifically a type of "Prompt Injection" or
"Adversarial Prompting." In this attack vector, the user (or an external attacker) provides malicious or deceptive input-in this case, via an image in a multimodal LLM-to bypass the model's intended constraints or to steer its logic toward an unintended outcome. By crafting an image that tricks the LLM into seeing
"acceptance criteria" that aren't actually there, the attacker manipulates the model's request processing to generate false validation results. This is different fromData Poisoning(Option A), which involves corrupting the training data before the model is even built. It is also distinct fromData Exfiltration(Option B), which aims to steal data from the model. In a testing environment, request manipulation is a significant risk because it can lead to "Silent Failures," where the AI reports that tests have passed or requirements are met based on deceptive input, thereby compromising the integrity of the entire Quality Assurance process.


NEW QUESTION # 25
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

Answer: C

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 # 26
Which option BEST differentiates the three prompting techniques?

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 # 27
Which technique MOST directly reduces hallucinations by grounding the model in project realities?

Answer: D

Explanation:
Hallucinations-where an LLM generates factually incorrect or nonsensical information-occur primarily when the model lacks sufficient specific information and "fills in the gaps" using probabilistic patterns from its training data. The most effective mitigation strategy is "grounding," which involves providing the model with detailed, project-specific context. By including technical specifications, existing API schemas, business rules, and identified constraints within the prompt, the tester restricts the model's operational space to the
"project realities." This ensures the model does not have to guess or improvise details about the System Under Test (SUT). In contrast, randomizing prompts (Option B) or relying on generic examples (Option C) increases the likelihood of inconsistent and inaccurate outputs. Furthermore, using "longer" or higher temperature settings (Option D) actually encourages creativity and randomness, which is the opposite of the precision required for testing and significantly increases the risk of hallucinations. Therefore, rich contextual grounding is the technical foundation for reliable AI-assisted test analysis.


NEW QUESTION # 28
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