Quiz CT-GenAI - ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0–High-quality PDF

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ISQI CT-GenAI Exam Syllabus Topics:

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

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Free PDF Quiz ISQI - CT-GenAI - Efficient ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 PDF

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q22-Q27):

NEW QUESTION # 22
A tester uploads crafted images that steer the LLM into validating non-existent acceptance criteria. Which attack vector is this?

Answer: B

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 # 23
What is a primary compliance concern related to Shadow AI in organizational test environments?

Answer: A

Explanation:
Shadow AIrefers to the use of artificial intelligence tools and services within an organization without explicit approval or oversight from the IT or Security departments. In a software testing environment, this often occurs when testers use public, consumer-grade LLMs to analyze proprietary code or sensitive requirement documents to speed up their work. The primary compliance concern is theviolation of established data handling and regulatory compliance standards(such as GDPR, HIPAA, or SOC2). When sensitive test data is fed into a "shadow" AI tool, that data may be stored on external servers or used to train future iterations of the model, leading to massive data leaks and legal exposure. This bypasses the organization's security controls, such as data masking and role-based access. Unlike "authorized" AI which undergoes a rigorous vendor risk assessment, Shadow AI creates an invisible attack surface. For a test organization, mitigating this risk involves providing approved, secure AI alternatives and implementing strict policies and monitoring to ensure that internal intellectual property is never processed by unvetted external services.


NEW QUESTION # 24
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 # 25
Which statement BEST contrasts interaction style and scope?

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 # 26
Which option BEST differentiates the three prompting techniques?

Answer: C

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
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