Vce CT-GenAI Download & CT-GenAI Interactive Practice Exam

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For exam applicants ActualPDF offers real ISQI CT-GenAI exam questions. There are three formats of the ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) practice material. These formats are PDF, desktop practice exam software, and web-based ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 (CT-GenAI) practice exam. With these questions, you can crack the ISQI CT-GenAI certification exam and save your time and money.

ISQI CT-GenAI Exam Syllabus Topics:

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

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ISQI ISTQB Certified Tester Testing with Generative AI (CT-GenAI) v1.0 Sample Questions (Q25-Q30):

NEW QUESTION # 25
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 # 26
Which of the following is NOT a valid form of LLM-driven test data generation?

Answer: D

Explanation:
Generative AI is exceptionally capable of creating structured and unstructured data, but its role is limited to
"generation" and "transformation," not infrastructure management or direct database administration. Creating production database backups (Option A) is a physical data management task involving the copying of actual stateful data from a server to storage; this is handled by database management systems (DBMS) and DevOps pipelines, not LLMs. Conversely, LLMs excel at the logic-based tasks listed in the other options. They can analyze requirements to identify and set boundary values (Option B) for input validation. They are also highly effective at creating combinatorial data (Option C), such as pairwise or all-combinations tables, by understanding the relationships between variables. Finally, one of the most powerful uses of GenAI in testing is generating synthetic datasets (Option D)-creating "fake" but realistically structured data that mimics production patterns without exposing Sensitive Personally Identifiable Information (SPII), thereby supporting privacy-compliant testing.


NEW QUESTION # 27
What does an embedding represent in an LLM?

Answer: D

Explanation:
Embeddingsare a fundamental concept in modern Natural Language Processing (NLP) and LLMs. They are high-dimensional numerical vectors-essentially lists of numbers-that represent the meaning (semantics) of a piece of text (a word, sentence, or document). Unlike traditional keyword matching, which looks for identical strings of characters, embeddings allow the model to understand the "closeness" of concepts. For example, in a vector space, the word "bug" would be mathematically closer to "defect" or "error" than to
"feature" or "requirement." This captures the semantic relationship between terms. This technology is the backbone of Retrieval-Augmented Generation (RAG) used in testing: when a tester queries a documentation set, the system converts the query into an embedding and looks for other chunks of text with similar vector values. This allows the AI to retrieve relevant context even if the exact keywords do not match. It is not about logical rules (Option C) or groups of tokens (Option A), but rather a mathematical representation of language that enables machines to process human meaning.


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: D


NEW QUESTION # 29
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 # 30
......

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