CT-GenAI Latest Exam Forum | CT-GenAI New Practice Questions

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ISQI CT-GenAI Exam Questions, applicants may study for and pass their desired certification exam. You may use TestKingFree's top CT-GenAI study resources to prepare for the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 exam. The ISQI CT-GenAI Exam Questions offered by TestKingFree are dependable and trustworthy sources of preparation. TestKingFree provides valid exam questions and answers for customers, and free updates for 365 days.

ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI CT-GenAI New Practice Questions & Valid CT-GenAI Test Vce

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q29-Q34):

NEW QUESTION # 29
How do tester responsibilities MOSTLY evolve when integrating GenAI into test processes?

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 # 30
A prompt section states: "Web checkout module v3.2; focus on coupon application; existing regression suite IDs T-112-T-150; recent defect ID BUG-431." Which component is this?

Answer: C

Explanation:
In a structured prompt, "Input Data" (or Reference Data) provides the specific subject matter that the model must process or analyze. The statement provided consists of factual identifiers and specific entities related to the System Under Test (SUT), such as the version number, the specific module name, reference IDs for existing tests, and a specific defect record. These elements serve as the raw material for the LLM's task. This differs from "Instructions" (Option C), which would be the command (e.g., "Analyze the following..."), or
"Constraints" (Option B), which would define the boundaries of the task (e.g., "Do not include T-115").
"Output Format" (Option D) would define how the result should look (e.g., "Provide a JSON list"). By clearly labeling this section as Input Data, the tester helps the model distinguish between the "what" (the data) and the "how" (the instructions), which is a key principle of structured prompt engineering aimed at improving the accuracy of AI-generated analysis.


NEW QUESTION # 31
Which setting can reduce variability by narrowing the sampling distribution during inference?

Answer: C

Explanation:
In the context of LLM inference,Temperatureis a hyperparameter that controls the randomness or
"creativity" of the model's output. When the temperature is set high, the model's probability distribution is
"flattened," meaning it is more likely to select less-probable tokens, leading to more diverse and sometimes unpredictable text. For software testing, where precision and repeatability are paramount,lowering the temperature(Option C) is the standard practice. A temperature of 0.0 makes the model "deterministic," meaning it will consistently choose the token with the highest probability. This narrows the sampling distribution and significantly reduces variability between runs. While a larger context window (Option D) allows the model to process more information, it does not directly control the randomness of token selection.
Similarly, the "learning rate" (Option B) is a parameter used during thetrainingorfine-tuningphase, not during inference. For generating test cases or scripts that must follow strict logic, a lower temperature ensures that the model remains focused and produces consistent results.


NEW QUESTION # 32
What is a primary compliance concern related to Shadow AI in organizational test environments?

Answer: B

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 # 33
Which concept refers to breaking text into smaller units for processing by LLMs?

Answer: D

Explanation:
Tokenizationis the foundational process by which an LLM breaks down raw text into smaller, manageable units called "tokens." These tokens can represent individual words, parts of words (sub-words), or even punctuation marks. This is a critical step because LLMs do not "read" words like humans do; they process numerical representations of these tokens. The way text is tokenized directly impacts the model's efficiency and its ability to understand complex technical terminology used in software testing. For example, a rare technical term might be broken into several sub-word tokens. This process is closely linked to theContext Window(Option C), which is the maximum number of tokens a model can "remember" or process at one time. WhileEmbeddings(Option B) are the numerical vectors that represent the meaning of these tokens, and theTransformer(Option A) is the underlying architecture that processes them, tokenization is the specific mechanism for initial text decomposition. Understanding tokenization is vital for testers when managing long requirement documents to ensure they do not exceed the model's limits.


NEW QUESTION # 34
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